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Sr Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you’ll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: * Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. * Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). * Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. * Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. * Retrain, maintain, and monitor models in production. * Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. * Construct optimized data pipelines to feed ML models. * Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. * Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. * Use programming languages like Python, Scala, or Java. Basic Qualifications: * Bachelor’s Degree * At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) * At least 4 years of experience programming with Python, Scala, or Java * At least 3 years of experience building, scaling, and optimizing ML systems * At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: * Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field * Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform * 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow * 3+ years of experience developing performant, resilient, and maintainable code * 3+ years of experience with data gathering and preparation for ML models * 3+ years of people management experience * ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents * 3+ years of experience building production-ready data pipelines that feed ML models * Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. Enterprise Platforms Technology (EPTech) comprises many of Capital One’s most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. What you’ll do in the role: * The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: * Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. * Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). * Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. * Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. * Retrain, maintain, and monitor models in production. * Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. * Construct optimized data pipelines to feed ML models. * Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. * Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. * Use programming languages like Python, Scala, or Java. Basic Qualifications: * Bachelor’s degree * At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) * At least 4 years of experience programming with Python, Scala, or Java * At least 3 years of experience building, scaling, and optimizing ML systems * At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: * Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field * Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform * 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow * 3+ years of experience developing performant, resilient, and maintainable code * 3+ years of experience with data gathering and preparation for ML models * 3+ years of people management experience * ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents * 3+ years of experience building production-ready data pipelines that feed ML models * Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you’ll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: * Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. * Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). * Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. * Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. * Retrain, maintain, and monitor models in production. * Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. * Construct optimized data pipelines to feed ML models. * Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. * Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. * Use programming languages like Python, Scala, or Java. Basic Qualifications: * Bachelor’s Degree * At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) * At least 4 years of experience programming with Python, Scala, or Java * At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: * Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field * 3+ years of experience building production-ready data pipelines that feed ML models * 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow * 2+ years of experience developing performant, resilient, and maintainable code * 2+ years of experience with data gathering and preparation for ML models * 2+ years of people leader experience * 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation * Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform * Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance * ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You’ll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You’ll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously apply the latest innovations and best practices in machine learning engineering. What you’ll do in the role The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: * Design, build, and deliver GenAI models and components that solve complex business problems, while working in collaboration with the Product and Data Science teams. * Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. * Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. * Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. * Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. * Retrain, maintain, and monitor models in production. * Construct optimized, scalable data pipelines to feed ML models. * Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. * Use programming languages like Python, Go, Scala or Java Basic Qualifications: * Bachelor’s Degree * At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) * At least 4 years of experience programming with Python, Scala, Go or Java * At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: * Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field * 3+ years of experience building production-ready data pipelines that feed ML models * 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow * 2+ years of experience developing performant, resilient, and maintainable code * 2+ years of experience with data gathering and preparation for ML models * 2+ years of people leader experience * 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation * Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform * Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance * ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer San Francisco, CA: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Senior Machine Learning Engineer (KServe + Kubernetes, building Kubernetes Clusters, PyTorch, TensorFlow) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you’ll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: * Design, build, and/or deploy ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. * Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). * Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. * Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. * Retrain, maintain, and monitor models in production. * Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. * Construct optimized data pipelines to feed ML models. * Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. * Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. * Use programming languages like Python, Scala, or Java. Basic Qualifications: * Bachelor’s Degree * At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply) * At least 3 years of experience designing and building data-intensive solutions using distributed computing * At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow) * At least 1 year of experience productionizing, monitoring, and maintaining models Preferred Qualifications: * 1+ years of experience building, scaling, and optimizing ML systems * 1+ years of experience with data gathering and preparation for ML models * 2+ years of experience developing performant, resilient, and maintainable code * Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform * Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field * 3+ years of experience with distributed file systems or multi-node database paradigms * Contributed to open source ML software * Authored/co-authored a paper on a ML technique, model, or proof of concept * 3+ years of experience building production-ready data pipelines that feed ML models * Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $161,800 - $184,600 for Senior Machine Learning Engineer New York, NY: $176,500 - $201,400 for Senior Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you’ll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: * Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. * Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). * Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. * Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. * Retrain, maintain, and monitor models in production. * Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. * Construct optimized data pipelines to feed ML models. * Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. * Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. * Use programming languages like Python, Scala, or Java. Basic Qualifications: * Bachelor’s Degree * At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply) * At least 3 years of experience designing and building data-intensive solutions using distributed computing * At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow) * At least 1 year of experience productionizing, monitoring, and maintaining models Preferred Qualifications: * 1+ years of experience building, scaling, and optimizing ML systems * 1+ years of experience with data gathering and preparation for ML models * 2+ years of experience developing performant, resilient, and maintainable code * Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform * Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field * 3+ years of experience with distributed file systems or multi-node database paradigms * Contributed to open source ML software * Authored/co-authored a paper on a ML technique, model, or proof of concept * 3+ years of experience building production-ready data pipelines that feed ML models * Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $161,800 - $184,600 for Senior Machine Learning Engineer New York, NY: $176,500 - $201,400 for Senior Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

As a Quant Investment Associate, you will be the analytical engine behind our Fixed Income platform. You will not only build the models but also interpret the data to provide strategic recommendations on portfolio construction, tactical asset allocation, and risk-adjusted optimization. This is a highly visible, front-office role where your work directly impacts the alpha generation of our Fixed Income mandates. Key Responsibilities Portfolio Construction & Optimization • Quantitative Modeling: Design and implement robust frameworks for multi-sector Fixed Income portfolio construction (including Sovereigns, Corporates, High Yield, and Securitized products)., • Optimization: Develop and refine optimization algorithms (e.g., Mean-Variance, Risk-Parity, or Factor-based) that account for transaction costs, liquidity constraints, and regulatory requirements (e.g., Basel IV/Solvency II)., • Alpha Generation: Utilize systematic signals and alternative data to identify relative value opportunities across global fixed-income markets., • Scenario Analysis: Conduct "Stress-Testing" and "What-If" simulations to quantify portfolio sensitivity to interest rate shocks, spread widening, or geopolitical events., • Custom Tooling: Build and maintain proprietary Python-based libraries for bond pricing, yield curve fitting, and attribution., • Visual Dashboards: Develop real-time dashboards (using Streamlit, Dash, or Tableau) to visualize portfolio exposures, risk concentrations, and performance drivers., • Ad-hoc Analytics: Rapidly respond to market events by performing deep-dive data analysis on specific sectors or idiosyncratic risks. Qualifications • Education: Masters or PhD in a STEM field (Financial Engineering, Mathematics, Physics, Statistics, or Computer Science)., • Experience: 3–10 years in a quantitative role within Fixed Income, ideally on the buy-side (Asset Management or Hedge Fund)., • Programming: Expert-level Python (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow). Experience with SQL and version control (Git) is essential., • Fixed Income Mastery: Deep understanding of duration/convexity, credit spreads, OAS, and the mechanics of structured products or derivatives (CDX, Swaps, Futures)., • Communication: Ability to bridge the gap between "Quant" and "PM"—explaining technical methodologies to non-technical stakeholders., • Commercial Acumen: A passion for markets and a drive to find profitable patterns in data., • Agility: Comfortable in a fast-paced trading floor environment where priorities can shift based on market volatility.

Staff ML Engineer New York, New York - hybrid $225,000 - $250,000 + bonus + LTI; $500,000-$700,000 total THE COMPANY Harnham is partnering with one of the most well known financial services companies, which is looking for an experienced AI / ML Engineer. This person will be at the forefront of building AI automation applications for new ways at identifying cost-saving opportunities. You'll partner with executive teams across the company and own generative AI, LLM and reinforcement learning modeling and deployment. RESPONSIBILITIES • Work closely with department heads to align on business goals, define machine learning challenges, and design effective ML solutions., • Develop and deploy machine learning models, including building data pipelines, orchestrating ML workflows, and optimizing system performance and reliability., • Maintain a robust codebase by writing well-tested code, covering both functional and non-functional aspects such as unit, integration, and load testing., • Stay informed on industry trends and advancements in generative AI, LLMs, agentic AI, deep learning, experiment with new model concepts, and run both offline and online evaluations., • Actively share knowledge through internal presentations, tech talks, and by promoting best practices in engineering and technology use. SKILLS AND EXPERIENCE • Advanced degree (Master’s or PhD) in Computer Science, Machine Learning or a related field., • Over 7 years of hands-on experience developing and deploying production-ready machine learning / deep learning / AI systems, covering the full model lifecycle—training, tuning, deployment, serving, and monitoring., • Enterprise-level application experience, ideally in real-time preferred., • Expertise in cloud infrastructure (especially AWS), machine learning orchestration tools like Kubeflow, TensorFlow, and the use of Feature Stores in live environments., • Experience as a tech-lead for scaling MLE teams preferred., • Commercial experience combination of big tech and scaling startups / scrappy environments a plus. BENEFITS The compensation package contains a base salary, bonus, LTI and a comprehensive benefits package. HOW TO APPLY Please register your interest by sending your CV via the Apply link on this page. KEY TERMS Artificial Intelligence | Generative AI | GenAI | Machine Learning | ML Engineer | Engineering | Deployment | Production | Real Time | Enterprise | Statistics | Mathematics | Financial Services | Banking | Python | Anomaly Detection | Fintech | Agentic AI | AI Agents

Senior Computer Vision Scientist Fully Remote - HQ in New York, New York $190,000-210,000 base salary + equity THE COMPANY Harnham is partnering with an innovative health-tech startup building patient-focused agentic AI, multimodal computer vision and LLM applications to advance capabilities in medical claims and medical imaging in hospitals. The AI science team builds scalable, data-driven solutions that personalize user experiences and equip care providers with actionable insights, all while working with large-scale infrastructure and diverse technologies. THE ROLE • You will be responsible for computer vision and multimodal model research, building and development for the company's agentic AI product across imaging centers and hospitals, • You will report directly to senior leadership and work closely on technical direction, • Own applied AI research and quickly build into production, particularly focusing on novel AI, computer vision and LLM applications, • You will directly work with production team on implement and design code and build out to production using various machine learning, imaging and LLM techniques, owning machine learning modeling, • You will play an integral role of building out the AI team and scaling out its product, • Act as a thought leader role for AI across the business, mentoring junior team members YOUR SKILLS AND EXPERIENCE The successful Senior Computer Vision Scientist will likely have the following skills and experience: • 5+ years of commercial experience preferred with a focus on applied machine learning and computer vision research, building production-grade models with NLP and LLMs ideally with voice, image and multi-modal systems, • Experience working in a scaling startup is preferred, • Expertise in Python (TensorFlow, PyTorch) for production-grade work, • Commercial experience building novel AI platforms with large datasets, • History of working with and managing real-time AI applications in production settings, • Cloud experience in AWS, Azure or GCP, • DevOps exposure with CI/CD pipelines preferred, • History of working on models from concept to production / end-to-end / 0-1, • Applied research background in a commercial setting required, • Publication and/or patent history highly preferred, • Experience in settings wearing multiple hats, • Domain experience in healthcare, health-tech, med-tech or similar a plus; EHR, EMR, claims, HEOR or other medical data exposure highly preferred, • History of partnering with non-technical stakeholders required, • Experience owning projects, • PhD degree in Medical Imaging, Computer Science, Biomedical Imaging or similar THE BENEFITS A competitive base salary of $190,000-210,000 + benefits + equity HOW TO APPLY Please register your interest by sending your résumé to Tim Jonas via the Apply link on this page. KEYWORDS Machine Learning | AI | Artificial Intelligence | Technology | Applied Research | Health-tech | Healthcare | Medical | Startup | Deployment | Production | LLMs | LLM | Large Language Models | GenAI | Gen AI | Generative AI | Voice Bots | Chatbots | Natural Language Processing | EHR | Electronic Health | Electronic Medical | HEOR | Claims Data | Patient | Hospital | Docker | Kubernetes | Weights & Biases | PyTorch | TensorFlow | Publications | Patents | Computer Vision | Medical Imaging | MRI | Multimodal

About Child Mind Institute We're dedicated to transforming the lives of children and families struggling with mental health and learning disorders by giving them the help they need. We've become the leading independent nonprofit in children's mental health by providing gold-standard evidence-based care, delivering educational resources to millions of families each year, training educators in underserved communities, and developing tomorrow's breakthrough treatments. Position Details: As part of the Center for Data Analytics, Innovation, and Rigor team, you will report to Technical Project Manager. You will will design and conduct advanced data analyses and develop machine learning models for use in mental health research. You will perform analyses on large datasets, collected using a range of multi-modal methods, including cognitive assessments (both paper-and-pencil and digitized formats), surveys, and other multimodal biometric modalities. You will be chiefly responsible for the design, documentation, and execution of experiments that advance our understanding of both existing data collection and assessment tools in the mental health space, as well as downstream analytic models that seek to apply these measurements.. This is an exempt, full-time, hybrid this position requires a minimum of four (4) days per week in the office, on a schedule determined by your supervisor in our NYC headquarters office. The in-office requirement and schedule are subject to change based on the needs of the program and the organization. You Will: * Design and implement analytical workflows including data preprocessing, featureengineering, model development, scientific analyses, data visualization, andvalidation. * Contribute to effective, scalable, well-documented code that is suitable for highperformance computing ecosystems and shared to GitHub, or equivalent,repositories for version control. * Perform organization, aggregation, pre-processing, and analysis of large datasetsusing high-performance computing infrastructures. * Apply both pre-trained and custom-made machine learning models and datamanipulation pipelines to research and clinical contexts, ensuring the responsiblehandling of sensitive data. * Write documentation and text for scientific or public dissemination of knowledge andattend and present at meetings. * Assist with the definition of project and collaboration objectives, tasks, anddeliverables at the outset of new efforts, and support ongoing monitoring ofprogress and alignment. * Perform additional job-related duties as assigned You Have: * Bachelor's in Neuroscience, Psychology, Engineering, Computer Science orequivalent combination of education and experience. * 5+ years of experience in data analysis and data science fundamentals (e.g.,algorithms, data structures, data visualization, machine learning), preferably in aclinical or research setting. * 5+ years of experience including education in at least one scientific programminglanguage (e.g., Python/R, Matlab) and related toolboxes or frameworks (e.g.,Tidyverse, Scipy, Sklearn, Polars, Pytorch) is required. * 3+ years of experience including education working in a Linux environment, usingversion control systems (e.g., GitHub), and software virtualization platforms (e.g.,Docker). * 3+ years of practical experience designing and developing ML and NLPtechnologies using ML and NLP toolchains (e.g., pytorch, tensorflow, SpaCy,HuggingFace, NLTK.) #LI-remote, #LI-on-site, #LI-hybrid. Our Benefits Our great compensation package and benefits include medical insurance, 401(k), paid parental leave, dependent care, discounted tickets and entertainment perks programs. For more information about our benefits, please visit our ___. Pay Range The salary range for the position is posted. Factors such as candidate's work experience, education/training, job-related skills, internal peer equity, as well as market and business considerations affect the salary offered within this range. In addition, this salary may be subject to a geographic adjustment (according to a specific city and state and depending on the role), if an authorization is granted to work outside of the location listed in this posting. EEO Disclaimer Child Mind Institute is committed to fostering an inclusive and equitable workplace where all individuals are treated with respect and dignity. We are proud to be an equal opportunity employer and prohibit discrimination and harassment of any kind. We provide equal employment opportunities to all employees and applicants for employment, regardless of race, color, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), sexual orientation, gender identity, gender expression, age, national origin, ancestry, citizenship status, marital status, military or veteran status, physical or mental disability, genetic information, medical condition, or any other characteristic protected by applicable federal, state, or local laws. In compliance with California law, we also prohibit discrimination based on reproductive health decision-making, status as a victim of domestic violence, sexual assault, or stalking, or any other category protected by the California Fair Employment and Housing Act (FEHA). In New York, we extend this prohibition to include status as a victim of domestic violence, familial status, or any other characteristic protected by the New York State Human Rights Law (NYSHRL). Child Mind Institute is dedicated to ensuring accessibility and reasonable accommodations for individuals with disabilities or medical conditions. If you require an accommodation to participate in the application process or perform your job, please contact our HR Department at ___ This policy applies to all aspects of employment, including recruitment, hiring, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, benefits, and training.

Scale AI is seeking a technically rigorous and driven AI Research Engineer to join our Enterprise Evaluations team. This high-impact role is critical to our mission of delivering the industry's leading GenAI Evaluation Suite. You will be a hands-on contributor to the core systems that ensure the safety, reliability, and continuous improvement of LLM-powered workflows and agents for the enterprise. The ideal candidate has a strong foundational knowledge of large language models, a passion for tackling complex evaluation challenges, and thrives in a dynamic, fast-paced research environment. We are looking for an engineer who can think outside the box, stays current with the latest literature in AI evaluation, and is passionate about integrating novel research ideas into our workflows to build best-in-class evaluation systems. • Partner with Scale's Operations team and enterprise customers to translate ambiguity into structured evaluation data, guiding the creation and maintenance of gold-standard human-rated datasets and expert rubrics that anchor AI evaluation systems., • Analyze feedback and collected data to identify patterns, refine evaluation frameworks, and establish iterative improvement loops that enhance the quality and relevance of human-curated assessments., • Design, research, and develop LLM-as-a-Judge autorater frameworks and AI-assisted evaluation systems. This includes creating models that critique, grade, and explain agent outputs (e.g., RLAIF, model-judging-model setups), along with scalable evaluation pipelines and diagnostic tools., • Pursue research initiatives that explore new methodologies for automatically analyzing, evaluating, and improving the behavior of enterprise agents, pushing the boundaries of how AI systems are assessed and optimized in real-world contexts., • Bachelor's degree in Computer Science, Electrical Engineering, a related field, or equivalent practical experience., • 2+ years of experience in Machine Learning or Applied Research, focused on applied ML systems or evaluation infrastructure., • Hands-on experience with Large Language Models (LLMs) and Generative AI in professional or research environments., • Strong understanding of frontier model evaluation methodologies and the current research landscape., • Proficiency in Python and major ML frameworks (e.g., PyTorch, TensorFlow)., • Solid engineering and statistical analysis foundation, with experience developing data-driven methods for assessing model quality., • Advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, or a related quantitative field., • Published research in leading ML or AI conferences such as NeurIPS, ICML, ICLR, or KDD., • Experience designing, building, or deploying LLM-as-a-Judge frameworks or other automated evaluation systems for complex models., • Experience collaborating with operations or external teams to define high-quality human annotator guidelines., • Expertise in ML research engineering, stochastic systems, observability, or LLM-powered applications for model evaluation and analysis., • Experience contributing to scalable pipelines that automate the evaluation and monitoring of large-scale models and agents. About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at . Please see the United States Department of Labor's ___ for additional information. We comply with the United States Department of Labor's ___. PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants' needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our ___ for additional information.

We're adding to our diverse team of experts and are looking to hire those who are committed to building a culture that enables the creation of innovative solutions for our business units and clients. We will consider a range of experience for this role and the offer will be commensurate with that. The Company As a member of Munich Re's US operations, we offer the financial strength and stability that comes with being part of the world's preeminent insurance and reinsurance brand. Our risk experts work together to assemble the right mix of products and services to help our clients stay competitive - from traditional reinsurance coverages, to niche and specialty reinsurance and insurance products. Job Overview: We are seeking a highly skilled Senior Data Engineer - DBX Platform (ML & Feature Engineering Focus) with a strong software engineering background to join our AI/ML Engineering GSI IT Team. In this role, you will be focused on building a DBX-based data platform that powers machine learning and advanced analytics. This role is ideal for someone who thrives at the intersection of data engineering, ML infrastructure, and software development, and is passionate about enabling scalable, production-grade feature engineering pipelines. Key Responsibilities: • Build and optimize DBX ETL/ELT pipelines for feature extraction, transformation, and loading from structured and unstructured data sources., • Collaborate with ML engineers, data scientists, and software developers to deliver reliable, reusable, and versioned feature sets., • Implement CI/CD pipelines, testing frameworks, and observability for data workflows., • Develop feature stores, metadata tracking, and lineage tools to support data Ops., • Ensure data quality, governance, and compliance across all data assets., • Optimize performance and cost-efficiency of DBX clusters and jobs for data workloads., • Contribute to the architecture and design of the data platform and feature engineering framework. Qualifications: • Strong proficiency in Python and relevant scripting languages, with experience in software development and scripting for DBX., • Expertise with libraries and frameworks (e.g., Pandas, Numpy, Scikit-Learn, TensorFlow, PyTorch, DBX, MLFlow, dvc, dbt) and the ability to select the right tools for the use case., • Experience building inference endpoints (APIs) and managing compute architecture for efficient model inference and data handling., • Very good Azure and data & AI technology skills - specifically: DBX/ Python, Azure DBX Store, Azure AI Search., • Experience with DevOps practices, including Git, CI/CD using tools such as Azure Pipelines, or similar., • Several years of experience in machine learning, data science, or a related field, with a strong understanding of statistics and data analysis., • Experience with Azure especially with data and ML services, containerization (Docker. Preferred Qualifications: • Advanced Degree: Master's degree in Computer Science, Engineering, Mathematics, with 5+ years of ML implementation experience or Ph.D. with 2+ years of hands-on ML Project experience., • Experience with Big Data: Strong proficiency with big data technologies such as Azure DBX and Spark., • Leadership Experience: Previous experience leading a team of data scientists or engineers. Benefits: • Competitive employee benefits, including comprehensive health insurance, dental and sports coverage, and opportunities for certified training., • Flexibility in work arrangements, including home office options and flexible working hours., • A positive, team-oriented environment that fosters mutual trust, creativity, and initiative., • Opportunities for career growth within a global, innovative framework., • A diverse, multicultural workplace with a strong emphasis on team collaboration and professional development. At Munich Re US, we see Diversity and Inclusion as a solution to the challenges and opportunities all around us. Our goal is to foster an inclusive culture and build a workforce that reflects the customers we serve and the communities in which we live and work. We strive to provide a workplace where all of our colleagues feel respected, valued and empowered to achieve their very best every day. We recruit and develop talent with a focus on providing our customers the most innovative products and services. We are an equal opportunity employer. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. The Company is open to considering candidates in Princeton, NJ. The salary range posted below applies to the Company's Princeton location. The base salary range anticipated for this position is $104,200 - $152,800 plus opportunity for company bonus based upon a percentage of eligible pay. In addition, the company makes available a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, 401k match, retirement savings plan, paid holidays and paid time off (PTO). The salary estimate displayed represents the typical salary range for candidates hired in this position in Princeton. Factors that may be used to determine your actual salary include your specific skills, how many years of experience you have and comparison to other employees already in this role. Most candidates will start in the bottom half of the range. ___ Save job

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, fueling the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we're amplifying access to high-quality data to drive progress toward Artificial General Intelligence (AGI). Building on our history of model evaluation with enterprise and government customers, we are expanding our capabilities to set new standards for both public and private evaluations. About This Role This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). The ideal candidate will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale's data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning. Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions. Ideally, you'd have: * Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow. You should also be skilled at rapidly interpreting research literature and turning new ideas into working prototypes. * A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.). * At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning. * Strong written and verbal communication skills, along with the ability to work effectively across teams. Nice to have: * Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX. * Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents. * Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar. * Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH. * Proficiency in cloud-based ML development, with experience in AWS or GCP environments. Our research interviews are designed to assess candidates' ability to prototype and debug ML models, their depth of understanding in research concepts, and their alignment with our organizational culture. We do not conduct LeetCode-style problem-solving assessments. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:$252,000—$315,000 USD PLEASE NOTE:Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at . Please see the United States Department of Labor's ___ for additional information. We comply with the United States Department of Labor's ___. PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants' needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our ___ for additional information.

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we're accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About the ACE team The Agent Capabilities & Environments (ACE) team, part of Scale's Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. About This Role This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions. • Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow. You should also be adept at interpreting research literature and quickly turning new ideas into prototypes., • A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.), • At least three years of experience addressing sophisticated ML problems, either in a research setting or product development., • Strong written and verbal communication skills and the ability to operate cross-functionally., • Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax., • Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents., • Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc., • Familiarity with agentic reasoning methods such as STaR and PLANSEARCH Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:$275,000—$350,000 USD PLEASE NOTE:Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at . Please see the United States Department of Labor's ___ for additional information. We comply with the United States Department of Labor's ___. PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants' needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our ___ for additional information.

The goal of a Senior Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and unique access to massive datasets to deliver improvements to our customers. Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like ___ and ___. Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications. • Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers., • Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics, • Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines, • Work with massive datasets to develop both generic models as well as fine tune models for specific products, • Build scalable machine learning infrastructure to automate and optimize our ML services, • Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations, • Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment, • This role will require an active security clearance or the ability to obtain a security clearance., • Extensive experience using computer vision, deep learning and deep reinforcement Learning, or natural language processing in a production environment, • Solid background in algorithms, data structures, and object-oriented programming, • Strong programing skills in Python, experience in Tensorflow or PyTorch, • Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization, • Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments, • Experience with computer vision, generative AI models, large language models, or agentic systems About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at . Please see the United States Department of Labor's ___ for additional information. We comply with the United States Department of Labor's ___. PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants' needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our ___ for additional information.

Lead applied ML engineering on Scale's Applied ML team, powering data infrastructure for leading agentic LLMs (ChatGPT, Gemini, Llama). You will build scalable multi-agent systems to validate agentic reasoning and behaviors, scale human expertise, and drive research into real-world agent reliability failures despite strong benchmarks, shipping production fixes. Ideal for exceptional engineers with deep research rigor and a relentless focus on practical, high-impact systems. You will iterate rapidly with data, leverage AI tools to accelerate development, and collaborate tightly across engineering, product, and research. If you excel at turning frontier agent research into reliable deployed systems, we want to hear from you. You will: • Build and deploy multi-agent systems for agentic reasoning validation, • Develop pipelines to detect errors and scale human judgment, • Combine classical ML, LLMs, and multi-agent techniques for reliability, • Lead research into agent failure modes and ship fixes, • Use AI tools to speed prototyping and iteration, • Build data-driven evaluations and deploy rapid improvements, • PhD or MSc in Computer Science, Mathematics, Statistics, or related field, • 3+ years shipping scaled production ML systems, • Demonstrated real-world impact, • Mastery of PyTorch, TensorFlow, JAX, or scikit-learn, • Deep expertise in agentic LLMs and multi-agent systems, • Strong software engineering and microservices (AWS/GCP), • Rapid, data-driven iteration, • Proficiency using AI tools to accelerate work, • Strong research depth with practical bias, • Experience prototyping agent evaluation/reliability systems, • Human-in-the-loop or annotation pipeline work, • Open-source contributions in agents, evaluation, or alignment About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at . Please see the United States Department of Labor's ___ for additional information. We comply with the United States Department of Labor's ___. PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants' needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our ___ for additional information.

We are seeking a strategic, technically strong Director of Decision Science to lead our Data Science and Business Intelligence teams. In this role, you will set the vision, guide a high-performing team, and drive Data Science and BI initiatives that influence decisions across the business. You will partner with cross-functional leaders to solve high-impact problems, elevate our self-serve BI capabilities, and embed data science into core business operations. This role reports to the VP of Data & Analytics. What You'll Do * Vision and Strategy Leadership: Define and own the multi-year Data Science and BI strategy and roadmap, ensuring direct alignment with enterprise priorities and quantifiable business outcomes (e.g., revenue growth, operational efficiency) * Team Leadership & Development: Recruit, mentor, and manage a high-performing, multi-disciplinary team of Data Scientists, BI Analysts, and BI Engineers, establishing a culture of innovation, operational excellence, and continuous professional development * Strategic Business Partnership: Act as the primary analytic partner to business unit leaders (Sales, Product, CS, Marketing, etc.) to define and deliver high-impact, decision-support data products, including predictive models and AI-powered self-service insights * Analytical Translation: Translate complex, ambiguous business challenges into structured analytical strategies, guiding teams to deliver high-quality, decision-ready solutions * Agile Delivery & Model Lifecycle: Drive an agile, iterative delivery process and oversee end-to-end predictive model lifecycle management, from ideation and validation through scalable deployment, monitoring, and optimization * Executive Communication & Storytelling: Communicate clear, actionable, and data-driven recommendations to all audiences, including senior and executive leadership, shaping strategy through compelling, tailored narratives * Data Platform Collaboration: Collaborate closely with Data Engineering and Analytics Engineering to design and scale enterprise data assets, features, and metrics that meet evolving analytical needs * Governance & Responsible AI: Establish and enforce best practices for metrics design, experimentation, and data governance. Champion data ethics, compliance, and responsible AI standards to promote transparency and trust * Enterprise Data Strategy: Partner with leaders across Data Platform, Data Product Management, and Data Governance to develop and execute the overarching Enterprise Data Strategy What You'll Need Minimum Qualifications * 8+ years of experience in Data Science, Machine Learning, or related analytical fields, including 3+ years leading high-performing technical teams * Advanced degree (MS/PhD) in a quantitative discipline (e.g., Computer Science, Statistics, Mathematics, Operations Research, or similar) * Demonstrated ability to translate ambiguous business problems into analytical frameworks and deliver measurable business impact * Experience in SaaS or B2B environments, especially where data products directly enable go-to-market, customer success, or product strategy * Hands-on experience deploying and operating ML models in production, including monitoring, optimization, and lifecycle management Preferred Qualifications * Deep expertise in statistical modeling, causal inference, experimentation design, and applied analytics to drive business decisions * Strong familiarity with modern data ecosystems, including cloud data warehouses (Snowflake, BigQuery), data modeling best practices, orchestration tools, enterprise BI platforms (e.g., Looker, Tableau, Sigma), and MLOps/ML lifecycle platforms * Experience building or applying NLP, recommendation engines, forecasting models, or other advanced analytical solutions * Strong programming skills in Python and proficiency with modern ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) * Demonstrated ability to design, govern, and scale standardized metrics, dashboards, and self-service BI workflows across business functions * Exceptional communication and stakeholder-management skills, with the ability to influence senior leaders and simplify complex concepts for diverse audiences * Proven success in hiring, developing, and scaling technical talent across Data Science and BI disciplines Working Conditions & Travel Requirements * Ability to travel to meet with customers and/or key stakeholders, product teams in other Workiva locations (up to 25%) * Reliable internet access for any period of time working remotely, not in a Workiva office How You'll Be Rewarded Salary range in the US: $177,000.00 - $284,000.00 A discretionary bonus typically paid annually Restricted Stock Units granted at time of hire 401(k) match and comprehensive employee benefits package The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors. Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic. Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email ___. Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards. Workiva supports employees in working where they work best - either from an office or remotely from any location within their country of employment. #LI-LP1

Senior Manager, Machine Learning Engineering As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you’ll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: * Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. * Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). * Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. * Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. * Retrain, maintain, and monitor models in production. * Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. * Construct optimized data pipelines to feed ML models. * Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. * Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. * Use programming languages like Python, Scala, or Java. Basic Qualifications: * Bachelor’s Degree * At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) * At least 4 years of experience programming with Python, Scala, or Java * At least 3 years of experience building, scaling, and optimizing ML systems * At least 2 years of experience leading teams developing ML solutions * At least 4 years of people management experience Preferred Qualifications: * Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field * 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow * 3+ years of experience developing performant, resilient, and maintainable code * 3+ years of experience with data gathering and preparation for ML models * Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform * 3+ years of experience building production-ready data pipelines that feed ML models * Ability to communicate complex technical concepts clearly to a variety of audiences * ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Mgr, Machine Learning Engineering New York, NY: $250,800 - $286,200 for Sr. Mgr, Machine Learning Engineering Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

What we're looking for:** - Professional experience in machine learning model development, Python & ML frameworks (PyTorch/TensorFlow), model deployment & MLOps - Strong communication skills - Ability to work independently in a remote environment How it works: This is not a specific job posting.

Pay Rate: $74.00 - $78.00 Negotiable based upon years of experience Global Financial Firm located in RUTHERFORD, NJ has an immediate contract opportunity for an experienced Data Scientist Summary: Immediate contract opportunity for an experienced professional Location: Rutherford, NJ Role involves establishing and implementing new or revised applications and programs in the Technology team Focus on data extraction and analysis from structured and unstructured sources Develop systems to clean results and build predictive and prescriptive models Implement models in a production environment Address complex problems involving financial data with a focus on credit risk management Open to learning new and advanced models in LLM and GenAI Responsibilities: Architect, design, and implement robust Python-based solutions Lead global technology teams across different time zones, enabling a hybrid work model Drive Object-Oriented Programming (OOPS) principle-based development Mentor team members to leverage advanced Python coding techniques and features Partner with multiple management teams for integration of functions and system enhancements Resolve high impact problems/projects through in-depth evaluation of complex business processes Provide expertise in applications programming and ensure design adheres to architecture blueprint Develop standards for coding, testing, debugging, and implementation Provide in-depth analysis with interpretive thinking for defining issues and developing solutions Serve as advisor or coach to mid-level developers and analysts Implement Unit Testing and TDD to ensure software quality and maintainability Assess risk when making business decisions, demonstrating consideration for the company's reputation Requirements: PhD or master’s degree in computer science, data science, statistics, mathematics, engineering, or related field 5 years of industry experience specializing in ML modeling, ranking, recommendations, or personalization systems 5 years of experience designing and developing scalable and reliable machine learning systems Strong background in ML/DL/LLM algorithms, model architectures, and training techniques Proficiency in Python, SQL, Spark, PySpark, TensorFlow or other analytical/model-building programming languages Ability to work independently and collaboratively within a team Preferred Skills: Experience in GenAI/LLMs projects Familiarity with distributed data/computing tools (e.g., Hadoop, Hive, Spark, MySQL) Background in financial business like banking, risk management Familiarity with capital markets and financial instruments and modeling

Senior Machine Learning Engineer (KServe Kubernetes, building Kubernetes Clusters, PyTorch, TensorFlow) As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. Basic Qualifications: Bachelor�

Senior Machine Learning Engineer (KServe + Kubernetes, building Kubernetes Clusters, PyTorch, TensorFlow). At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow).

Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. Please note that this salary information is solely for candidates hire

Job Description AI Specialist Technical Expertise: · Proficiency in AI/ML frameworks (TensorFlow, PyTorch), Generative AI models, and NLP techniques, Adobe’ suite of products · Hands-on experience with cloud AI services (AWS, Azure AI, Google Cloud AI) and tools like GitHub Copilot. · Strong programming skills in Python and familiarity with APIs for integration. Domain Knowledge: · Understanding of digital experience platforms, marketing technology stacks (Adobe, Sitecore, Salesforce, etc.), and data ecosystems. · Exposure to personalization engines, recommendation systems, and conversational AI. Roles & Responsibilities The AI Specialist will be a key contributor in Company's DX Practice, focusing on designing, developing, and deploying AI-driven solutions that enhance customer experience, marketing technology, and digital transformation initiatives. This role demands strong technical expertise in AI/ML and Generative AI, combined with a deep understanding of digital experience platforms and enterprise ecosystems. Solution Development: · Design and implement AI-powered solutions for personalization, automation, and customer engagement across marketing, commerce, and service channels. · Develop and optimize machine learning models for predictive analytics, recommendation engines, and conversational AI. Innovation & Experimentation: · Drive PoC and MVP-led experimentation for AI use cases in DX, leveraging frameworks · Collaborate with design studios and engineering teams for rapid prototyping and creative AI engineering. Integration & Deployment: · Integrate AI capabilities with enterprise platforms such as Adobe Experience Cloud, AEM, CDP, and marketing automation tools. · Ensure compliance with Responsible AI frameworks, geo-specific regulations, and data privacy standards. Collaboration & Advisory: · Work closely with DX Advisors, Experience Designers, and Martech SMEs to align AI solutions with client objectives. · Participate in client workshops and strategic discussions to position AI capabilities effectively. Generic Managerial Skills, If any · Strong analytical and problem-solving abilities. · Excellent communication and stakeholder management skills. Ability to work independently and in cross-functional teams. Education Bachelors Degree

Job Description Location: New York, NY Our client is a distinguished global provider of legal services. This is a full-time, direct hire onsite role. We are seeking an accomplished Innovation AI/ML Data Scientist to architect and implement advanced, data-driven solutions that improve quality, efficiency, and decision-making throughout the firm's legal services and operations. In close collaboration with attorneys, innovation leaders, and cross-functional enterprise teams, you will translate complex legal and organizational challenges into secure, scalable artificial intelligence and machine learning solutions. You will oversee the entire solution lifecycle—from experimentation and performance measurement through deployment and ongoing monitoring. Responsibilities include curating and governing datasets, establishing successful criteria for accuracy and safety, and developing production-ready models and applications, including both large language model (LLM)-based and traditional machine learning approaches. Reporting to the Director of Innovation Engineering, you will play a pivotal role in advancing and expanding the firm’s data science initiatives—streamlining routine processes, producing actionable insights, and enabling new capabilities that enhance client outcomes and organizational efficacy. The firm maintains rigorous standards for performance and cultivates a growth-oriented culture. Individuals who demonstrate initiative and commitment will have opportunities for expanded responsibilities and long-term career growth within the organization. Must be a U.S. Citizen or Green Card holder authorized to work in the U.S. – sponsorship is not available Desired Qualifications and Experience: · Bachelor’s degree in computer science, statistics, mathematics, engineering, or a related quantitative discipline required; preference given to candidates with advanced degrees. · Minimum of seven years delivering AI/ML solutions within professional services, financial, legal, or technical sectors, featuring significant cross-functional collaboration among legal and business stakeholders. · Proficient in Python for data analysis, statistical modeling, and AI/ML prototyping (including pandas, PyTorch, TensorFlow); skilled in advanced SQL; experienced in constructing data pipelines and preparing both structured and unstructured datasets. · AI & LLM Application Knowledge: Practical experience with AI platforms and tools, such as OpenAI, Microsoft Azure OpenAI, Hugging Face, LangFlow, and LangChain; strong understanding of vector databases, retrieval methods for unstructured data, and knowledge graph–based applications. · Demonstrated ability to design and implement evaluation frameworks for enterprise AI solutions, including monitoring accuracy, robustness, hallucinations, bias, and fairness. · Experienced with data visualization tools including Plotly, Tableau, and Microsoft Power BI; capable of translating complex findings into actionable insights. · Familiarity with legal use cases such as due diligence, contract review, and legal research; prior experience in a law firm or Microsoft Azure environment is preferred. · Proven track record mentoring colleagues, collaborating across multidisciplinary teams, and promoting widespread adoption of AI solutions. Summary of Responsibilities: · Translate Legal Workflows into AI Solutions – Facilitate discovery sessions with attorneys and practice stakeholders to systematically map end-to-end legal processes into actionable data and LLM requirements, along with measurable success criteria and evaluation plans aligned with business objectives. · Design and Prototype Intelligent Applications – Architect and assess AI/ML solutions, including retrieval-augmented generation (RAG) pipelines, vector database integrations, and orchestration frameworks, to determine feasibility, optimize performance, and ensure scalability. · Deliver High-Impact Legal Use Cases – Develop robust solutions addressing contract analysis, due diligence review, document summarization, large-scale agreement comparisons, and other practice-driven requirements. · Establish Evaluation & Governance Frameworks – Define and implement rigorous production-ready evaluation standards, focusing on accuracy, robustness, bias and fairness, hallucination mitigation, and business-aligned performance benchmarks, with ongoing monitoring protocols. · Develop Reusable Assets & Libraries – Create comprehensive internal libraries, documentation, and standardized development practices to promote efficient and scalable delivery of AI solutions. · Collaborate Across Technical & Legal Teams – Engage closely with embedded data science teams and business stakeholders to align priorities, co-create solutions, and drive successful adoption across practice groups. · Communicate Insights with Clarity – Present evaluation results, trade-offs, risks, and projected outcomes in clear, accessible terms for non-technical stakeholders, including attorneys and business leaders. · Advance Innovation Strategy – Assess emerging technologies, open-source tools, and vendor platforms to inform technology strategy, contribute to internal thought leadership, and support firmwide AI enablement initiatives. · Mentor & Enable Growth – Guide and mentor data scientists and engineers, encourage knowledge sharing, and strengthen the organization's AI capabilities. How to Apply: Our client has hired us to help facilitate the initial interview and recruiting process. Please attach your current version of your resume and make sure you complete our initial pre-screening questions that will be used for determining which applicants will be considered at this time. Thank you for your interest. Company DescriptionWe Place People is a premiere Executive Search Firm working with leading companies nationwide. We have a direct relationship with our clients and a 95% hire rate! We differentiate ourselves from other firms & work closely with our candidates throughout the interview process. WE PLACE PEOPLE is what we do best!We Place People is a premiere Executive Search Firm working with leading companies nationwide. We have a direct relationship with our clients and a 95% hire rate! We differentiate ourselves from other firms & work closely with our candidates throughout the interview process. WE PLACE PEOPLE is what we do best!

Job DescriptionCompany Description Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds to pursue greater human understanding, pioneering discoveries, and service to society. The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through seventeen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good. Job Description The School of Professional Studies seeks experienced industry professionals to serve as part-time Lecturer for a graduate-level course in Applied Deep Learning and AI. This advanced course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models. Lecturers are the primary instructors for courses and an invaluable component of the faculty community. Responsibilities * Lead class lectures, instructional activities, and classroom discussions. Attend all class sessions. * Monitor and address student concerns and inquiries. * Evaluate and grade student work and assessments. * Conduct office hours. Qualifications Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training and a record of accomplishment as practitioners in an applied industry setting. Requirements • Doctorate degree in Computer Science, Data Science, or a related field., • Proficient in Python and familiar with deep learning frameworks (e.g., TensorFlow, PyTorch)., • Deep Learning Knowledge: Strong understanding of CNNs, RNNs, LLMs, Reinforcement Learning, and model evaluation techniques. 10+ years of related professional experience. * 2+ years of University teaching experience, ideally at the graduate level. * Strong verbal and written skills for explaining concepts clearly. Additional Information Salary range: $11,000 - $13,000 per semester long course Please submit a resume inclusive of university teaching experience. All your information will be kept confidential according to EEO guidelines. Columbia University is an Equal Opportunity Employer / Disability / Veteran

Job Description Company Overview: TrueGrowth is a forward-thinking company committed to empowering organizations with cutting-edge AI solutions. Our team thrives on innovation, collaboration, and a passion for transforming ideas into impactful technology. We are looking for an AI Engineer to help us shape the future of intelligent systems. Position Summary: As an AI Engineer, you will design and implement advanced AI models and algorithms that drive our products and services. Youll work closely with data scientists and product teams to develop solutions that leverage machine learning, natural language processing, and more. How to Apply: If youre ready to push the boundaries of AI technology, we want to hear from you! Please send a message on Instagram to @genomiller26! Key Responsibilities: * Design and develop AI models and algorithms to solve complex problems. * Collaborate with cross-functional teams to integrate AI capabilities into existing products. * Analyze large datasets to extract insights and improve model performance. * Stay current with industry trends and advancements in AI technology. Qualifications: * Strong experience in machine learning frameworks (TensorFlow, PyTorch, etc.). * Proficiency in programming languages such as Python, Java, or C++. * Familiarity with natural language processing and computer vision techniques. * Excellent problem-solving skills and a passion for AI innovation. Why Join Us? * Flexible Work Environment: Enjoy the freedom of remote work while engaging in groundbreaking AI projects. * Growth Opportunities: We believe in nurturing talent. Optional career coaching services are available to support your professional development at no risk—if you dont find it beneficial, theres no obligation to continue. * Dynamic Team Culture: Be part of a team that values creativity and collaboration, where your contributions are pivotal to our success. How to Apply: If youre ready to push the boundaries of AI technology, we want to hear from you! Please SEND A MESSAGE on Instagram to @genomiller26!

Job Description Senior Manager, Commercial Analytics & Data ScienceHow you’ll make an impact: • You will build machine learning models to answer key business questions impacting strategy and marketing spend allocation, • You will perform feature engineering and contribute to our feature store, • You will lead dashboard development and lead development of enhanced visualization toolsResponsibilities: What you’ll do:, • Build advanced machine learning models to inform marketing tactics (examples: adaptive clustering, reinforcement learning, regression modeling, price elasticity modeling etc.), • Execute sophisticated quantitative analyses and descriptive modeling to answer key business questions to shape business strategy., • Use enhanced python based graphical visualizations to deliver key insights to leadership., • Research and implement novel modeling techniques to solve complex business problems., • Develop analytics databases & lead development and maintenance of automated dashboards for AB experiment results., • Develop complex SQL queries combining data from a wide variety of sources in preparation of feature engineering or to perform analysis of business questions., • Measure incrementality of paid media campaigns using matched market testing.Requirements: What you’ll need:, • Bachelor’s or Master's degree in data science, computer science, statistics, engineering, or related quantitative field., • Strong prior experience in data science, business & statistical analytics., • 7+ years of experience in related field., • Strong background in SQL & Python, R is a plus., • Experience in machine learning algorithms & libraries. Examples: CausalML/EconML, TensorFlow, PyTorch, Keras, sk-learn, seaborn, LSTM, RNN., • Experience in visualization tools. Examples: Databrick AI/BI, Rshiny, Dash, Tableau., • Familiarity with Databricks platform is a plus., • Willingness to take initiative and to follow through on projects., • Excellent time management skills with the ability to prioritize and multi-task, and work under shifting deadlines in a fast-paced environment., • Must have legal right to work in the U.S.Compensation:, • $100 to $105/hr. (W2)Job req# 1654-1

Job DescriptionResponsibilities • Lead the Machine Learning and Applied Science team through strategic roadmaps to support business initiatives with high impact., • Guide the teams technically, engaging in architecture definition, implementation of best practices, and troubleshooting when needed., • Mange the team through all phases of machine learning development, from concept and design to deployment and monitoring., • Stay ahead of state-of-the-art research and industry trends in machine learning, computer vision, pricing algorithms, and related fields., • Collaborate with other Engineering and Product teams to evaluate requirements and use cases for new systems., • Invest in the career development of the team members, develop future leaders, and create a culture of cohesion and teamwork., • Participate in talent acquisition processes to ensure that we have world class engineers across all skill and experience levels., • Establish metrics to measure the productivity of the team, hold people accountable and identify people issues early.Requirements and Qualifications, • PhD or MS in Computer Science, Statistics, Operations Research or a relevant quantitative discipline. PhD is strongly preferred., • 5+ years of industry experience, with at least 1+ years of experience leading machine learning teams preferably in computer vision or pricing domain., • 2+ years of experience as a hands-on senior, staff, or principal engineer with demonstrated end-to-end ownership of a machine learning project before transitioning into managing teams., • Expert level knowledge in Python and SQL for model development and statistical analysis., • Proficiency in ML frameworks such as TensorFlow, PyTorch, or scikit-learn., • Proficiency in edge computing, distributed systems, and cloud platforms (e.g., AWS, GCP, Azure)., • Strong foundation in data structures, algorithms, and optimization techniques., • Experience with common program management (MS Project, Visio, etc.) and productivity (Confluence, Jira, etc.) tools., • Excellent written and verbal communication skills with a proven ability to present complex technical information in a clear and concise manner to a variety of audiences., • Previous experience working inside innovative, high-growth environments is a plus., • Publications or patents in relevant fields such as machine learning, computer vision, or optimization is a plus.Company DescriptionStaffSource is committed to creating long-term partnerships where we can truly affect both clients’ and candidates’ lives for the better. Our success is rooted in matching talented professionals with environments that suit their goals, personalities, lifestyles, and skills. A perfect placement allows candidates to thrive in their new role and hiring managers to fill their openings with top talent that aligns with both the needs of their team and the company’s culture. Founded in 2003, StaffSource now has a 20+ year company history with business units operating within Information Technology, Accounting and Finance, Sales, Executive Search and more. Still headquartered in Knoxville, TN and lead by our founder John Sharpe, we are now serving clients and candidates all across the United States. We conduct national, regional, and localized talent searches based on the specific needs of each client and the unique goals of top talent everywhere.StaffSource is committed to creating long-term partnerships where we can truly affect both clients’ and candidates’ lives for the better. Our success is rooted in matching talented professionals with environments that suit their goals, personalities, lifestyles, and skills. A perfect placement allows candidates to thrive in their new role and hiring managers to fill their openings with top talent that aligns with both the needs of their team and the company’s culture.\r\n\r\n Founded in 2003, StaffSource now has a 20+ year company history with business units operating within Information Technology, Accounting and Finance, Sales, Executive Search and more. Still headquartered in Knoxville, TN and lead by our founder John Sharpe, we are now serving clients and candidates all across the United States. We conduct national, regional, and localized talent searches based on the specific needs of each client and the unique goals of top talent everywhere.

Job Description Stuut is transforming accounts receivable for B2B companies—making collections smarter and faster for companies that have historically relied on manual processes that are labor intensive and costly. Our platform is gaining traction with finance teams across industrials, chemicals, and manufacturing sectors from Fortune 10 brands to scaling midmarkets. We're backed by top-tier investors including a16z, Khosla, Activant, 1984 Ventures and Page One. The Role We’re hiring a Member of Technical Staff – AI/ML to design, build, and deploy AI-powered systems that solve real-world financial operations challenges. You’ll take state-of-the-art AI research and translate it into production-grade features that deliver measurable customer impact. From intelligent invoice matching to automated payment reconciliation, you’ll create scalable, reliable AI applications that integrate seamlessly into our platform. This is a hands-on role for an engineer who thrives at the intersection of AI innovation and practical business application — turning cutting-edge models into real-world value for midmarket CFOs What You’ll Do * Build & Deploy: Create production-ready AI applications that directly address customer financial workflow challenges. * Optimize AI Features: Implement and refine capabilities like invoice matching, payment reconciliation, and financial document processing. * Adapt & Fine-Tune Models: Apply and optimize LLMs, RAG systems, and other AI architectures for specific financial use cases. * Engineer for Scale: Develop robust AI pipelines from data ingestion through inference, ensuring reliability, scalability, and maintainability. * Automate Workflows: Design AI-powered automations that reduce manual effort in finance operations. * Collaborate Cross-Functionally: Partner with Product, Engineering, and customers to translate business needs into effective AI solutions. * Measure Impact: Create evaluation frameworks to track AI system performance and quantify business outcomes. You Might Be a Fit If You… * Have 5+ years of software engineering experience, with at least 2 years focused on applied AI/ML in production environments. * Have integrated and fine-tuned LLMs for real-world applications. * Have built retrieval-augmented generation (RAG) systems for document-intensive workflows. * Understand MLOps best practices, from model deployment pipelines to monitoring and A/B testing. * Are fluent in Python and experienced with PyTorch, TensorFlow, and Transformers. * Have shipped AI products that solved real business problems — not just prototypes. * Have strong experience with data preprocessing and feature engineering for AI. * Can translate business requirements into clear, impactful technical solutions. * Have experience integrating AI systems into existing enterprise platforms. Compensation * Top-of-market salary and equity package * Benefits (for U.S.-based full-time employees) * Medical, dental & vision insurance coverage for you * 401(k) & Match * Equity * Flexible PTO * Parental Leave Compensation Range: $220K - $375K

Job Description About Synaptrix Labs Inc. Synaptrix is on a mission to revolutionize brain-computer interfaces through non-invasive approaches. We believe that the power to diagnose and treat neurological conditions safely, and to expand human potential, will become a reality with the right fusion of deep learning, signal processing, and computational neuroscience. We're seeking passionate, creative, and technically driven interns to join our team across multiple disciplines: Software Engineering, Hardware Engineering, Machine Learning Research, Industrial Design, and UI/UX Design. If you're excited about building the future of brain-computer interfaces and want to work at the intersection of neuroscience, AI, and human experience, we want to hear from you. Responsibilities: Depending on your background and interests, you may: • Contribute to development of real-time software systems for neural signal processing and data visualization., • Build and prototype non-invasive EEG-based BCI hardware, from electrode arrays to embedded microcontroller systems., • Design, train, and evaluate machine learning models for EEG/EMG decoding, denoising, and signal enhancement., • Develop intuitive interfaces and user experiences for individuals with mobility or communication impairments., • Collaborate with the design team to prototype wearable and ergonomic form factors for next-generation neurotech devices., • Support data collection and analysis in research sessions, assisting with experimental setup and instrumentation., • Participate in cross-functional design reviews and brainstorming sessions that bring together neuroscience, AI, and engineering., • Currently pursuing a Bachelor's or Master's degree in Computer Science, Electrical Engineering, Biomedical Engineering, Neuroscience, Mechanical/Industrial Design, or a related field., • Demonstrated experience with one or more of the following:, • Software: Python, C++, or JavaScript, • Machine Learning: PyTorch, TensorFlow, or JAX, • Hardware: Arduino, PCB design, or embedded systems, • Design: Figma, Fusion 360, SolidWorks, or similar tools, • Strong problem-solving ability and curiosity about neuroscience, AI, or human-machine interfaces., • Excellent communication and teamwork skills; thrives in a fast-paced, interdisciplinary environment., • Prior experience with EEG/EMG systems, biosignal acquisition, or data analysis., • Familiarity with deep learning for time-series or multimodal data., • Experience designing assistive devices, wearables, or medical technology., • Portfolio, GitHub, or research projects demonstrating creative technical work. At Synaptrix Labs, we celebrate curiosity, open collaboration, and scientific rigor. Our interdisciplinary team spans neuroscience, AI, and clinical research, and we are united by the belief that non-invasive BCI is the key to unlocking a new era in healthcare, accessibility, and human augmentation. As an intern, you'll work directly alongside engineers, researchers, and designers who are defining the next generation of brain-computer interfaces. You'll be encouraged to explore bold ideas, experiment freely, and take ownership of meaningful projects from day one. What We Offer: • Hands-on experience in one of the world's most exciting emerging fields: brain-computer interfaces, • Opportunity to contribute to technology that directly improves lives, • Mentorship from scientists, engineers, and founders building real neurotechnology products, • Dynamic NYC workspace blending lab, workshop, and creative studio environments, • Potential for full-time conversion after graduation

Job DescriptionCompany Description SOCOTEC is a provider of niche consulting services within the architecture, engineering and construction industries focusing on high-performance buildings and specialty structures. Through an integrated, holistic approach, our professionals provide solutions for building envelope, energy efficiency, sustainability, code advisory and construction advisory projects SOCOTEC’s combination of expertise, experience and methods are increasingly recognized as best-in-class fueling significant growth. SOCOTEC is proud to announce that it has been officially certified as a Great Place to Work by the renowned global authority on workplace culture, Great Place to Work®. Job Description As an Applied AI Engineer at SOCOTEC, you will be responsible for designing, developing, and deploying AI models that enhance our chatbot program and other AI-driven solutions. You will collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders, to integrate advanced AI technologies that address real-world challenges within our industry. You will focus on building scalable, efficient, and accurate machine learning pipelines while staying at the forefront of the latest AI advancements. • Developing AI Models: Design and implement machine learning models tailored to natural language processing (NLP) and other AI applications within our chatbot and enterprise systems., • Collaborating with Cross-Functional Teams: Work closely with product managers, data scientists, and software engineers to translate business requirements into intelligent AI-driven solutions., • Building and Maintaining AI Pipelines: Create and optimize end-to-end machine learning pipelines, from data preprocessing to model deployment, ensuring scalability and performance., • Optimizing Model Performance: Continuously fine-tune and improve the accuracy, speed, and robustness of AI models, leveraging techniques like hyperparameter tuning and model optimization., • Implementing Best Practices in AI/ML Development: Ensure the reliability and quality of AI models by following best practices for training, testing, and model evaluation., • Integrating AI Solutions: Work closely with backend engineers to integrate AI models into production environments using tools like LangChain and cloud services., • Staying Updated with AI/ML Trends: Keep up with the latest developments in AI, machine learning, and natural language processing, applying new techniques to improve our technology stack., • Documentation: Provide detailed documentation of AI workflows, models, and algorithms to facilitate collaboration and ensure long-term maintainability., • Perform all duties as assigned by your managerQualifications, • Proficiency in Python and experience with AI/ML libraries such as Scikit-learn, TensorFlow, PyTorch, and Hugging Face., • Strong understanding of natural language processing (NLP) and experience building chatbot or conversational AI systems., • Hands-on experience with AI model development, including data preprocessing, model training, evaluation, and deployment., • Familiarity with AI/ML tools like Jupyter Notebook and cloud platforms such as AWS, GCP, or Azure., • Experience with version control systems (e.g., Git, GitHub) and agile development methodologies., • Ability to optimize model performance and scale AI solutions for production use., • Experience with frameworks like LangChain for building conversational AI workflows., • Familiarity with reinforcement learning, transfer learning, or other advanced AI techniques., • Knowledge of model monitoring, A/B testing, and continuous improvement strategies for AI systems., • Experience with containerization tools like Docker for deploying AI solutions. Additional Information For more information, please visit Job Type: Full-Time, onsite 4 days a week Salary: $90k-$150k All your information will be kept confidential according to EEO guidelines. Socotec is an equal opportunity employer committed to diversity and inclusion in the workplace. SOCOTEC is an Equal Opportunity Employer.

Job Description Do you want to be part of the core team that is building the next one-of-a-kind tech companies that can potentially change your career's trajectory?! We are striving to build the future of AI for consumer enterprises, and our founders need your help as we are growing. Come join us and leave your mark! Our founders are looking for a highly ambitious and highly technical Head of Technology to lead the strategy, development, and integration of AI systems across all layers of the AI stack. This senior executive role requires deep technical expertise in both AI/ML and full-stack development, combined with strong leadership, strategic vision, and business acumen. They are familiar with startup environments and bridge the gap between technical teams and business stakeholders to drive innovation and deliver robust, scalable, and ethical AI-powered products. RESPONSIBILITIES • Hands-on Technical Implementation, Architecting, and Oversight, • Design and implement end-to-end systems: Guide and implement the AI-Native platform of Verneek, as well as its solution suite, fullstack: from backend infrastructure to data ingestion, model training and inference, to front-end interfaces and applications., • Architect scalable solutions: Ensure the development of robust, scalable, secure, and maintainable full-stack systems that can deploy and run AI models in production at scale., • Evaluate and select technology: Assess and recommend the use of appropriate AI frameworks (e.g., PyTorch, TensorFlow), cloud platforms (e.g., AWS, Azure), and other tools and vendors., • Enforce best practices: Drive and enforce coding standards, technical architecture patterns (e.g., microservices), and quality control mechanisms., • Strategic leadership and overall vision, • Define and execute the technology strategy: Set the vision, roadmap, and strategic priorities for how Verneek’s fullstack technologies will create business value, improve products, and drive operational efficiency., • Align with business objectives: Collaborate with senior leadership and product teams to translate business needs into technical specifications and viable AI solutions., • Team leadership and management, • Manage projects: Oversee project planning, resource allocation, and budget management to ensure successful, on-time, and high-quality delivery., • Build and mentor teams: Recruit, develop, and manage high-performing teams of Software/AI/ML engineers, full-stack developers, and MLOps specialists, and beyond., • Foster a culture of innovation while delivering quickly: Encourage experimentation, continuous learning, and adaptability to emerging technologies and trends. Requirements • Advanced hands-on technical expertise: Demonstrated hands-on expertise in both AI/ML engineering (including LLMs, computer vision, NLP, etc.) and modern full-stack development as an individual contributor., • Experience with cloud and MLOps: Proficiency with cloud environments (AWS, GCP, Azure) and scaling MLOps for model training, deployment, inference, and monitoring., • Leadership experience: Proven track record of leading and managing engineering or AI/ML teams., • Strategic thinking: Ability to develop a long-term technical vision and align it with overall product and business strategy., • Excellent internal and external communication: The ability to communicate complex technical concepts to both technical and non-technical audiences, including executive leadership and Verneek partners., • Startup Experience:. Prior track record of having helped built series of technologies at a deeply technical startup. MINIMUM QUALIFICATIONS • Bachelor’s Degree in CS/CE/AI other technical fields, • Work authorization in the USA at the time of hire At Verneek, we are very determined to build a company that promotes diversity of thought that comes from the diversity of the individuals on the team! Candidates from any gender identity, race, color, religion, sexual orientation, national origin, veteran, or disability status are highly encouraged to apply. Benefits Stellar medical, dental, vision, disability, and life insurance Daily private Chef lunch, curated to personal diets Transportation Benefits 401K matching contributions Flexible PTO Visa/Green Card Sponsorship Career growth support through sponsoring learning opportunities and mentorship About Verneek Verneek is an early-stage deep-tech AI startup, based in the NYC area, founded by a team of leading AI research scientists and backed by a group of world-renowned business and scientific luminaries. Our mission is to build the most helpful AI for anyone, anywhere, at anytime. We are obsessed with what we do and we have fun doing it. Read more about verneek here: and make sure to watch all our introductory videos and yearly recaps here: . Verneek Culture It’s often hard to put “culture” into words, perhaps you can get a visual sense of our culture here: . We all obsessively love what we do, care about each other, share all sorts of meals together, celebrate all kinds of events together, and work tirelessly with the excitement of making a difference through AI innovation. We are enjoying the journey, and going through all the ups and downs together. Although we have come a very long way in setting the foundations of our unique company, but we still have ways to go and you can help shape our culture! The core Verneek team plays a crucial role in further shaping the culture of the company moving forward. We are looking for highly ambitious and tremendously driven individuals who can take the lead in driving various aspects of the company, and help us shape its lasting impact. Annual Salary Range: [tbd]

Job Description Tiger Analytics is seeking a highly skilled Senior AI Engineer to join our dynamic team. In this role, you will design, develop, and implement cutting-edge AI solutions that drive business value for our clients. You will leverage advanced machine learning techniques, frameworks, and large datasets to create innovative products and solutions. As a Senior AI Engineer, you will work closely with cross-functional teams to identify project requirements, define success metrics, and maintain high-quality standards. Your expertise in AI will play a critical role in shaping our product offerings and ensuring the successful deployment of AI models in production. The successful candidate will have a solid background in artificial intelligence, experience with various ML frameworks, and a passion for solving complex business problems. Requirements Key Responsibilities: • Design and implement AI algorithms and models to solve real-world problems., • Collaborate with data scientists, data engineers, and business analysts to understand project requirements and propose relevant AI solutions., • Monitor and analyze the performance of AI models in production and iterate on them to improve outcomes., • Develop technical documentation as well as customer-facing documentation and presentations., • Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field., • 5+ years of experience in AI/ML development with demonstrated success in deploying machine learning models to production., • Proficiency in programming languages such as Python and familiarity with libraries such as TensorFlow, PyTorch, and Scikit-learn., • Experience with cloud-based AI services such as AWS, Google Cloud, or Azure., • Strong analytical and problem-solving skills, with a proven ability to interpret business requirements into technical solutions., • Experience with natural language processing (NLP) or computer vision applications., • Knowledge of MLOps principles and tools. This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. #LI-remote

Job Description:\n\nAbout Phaidra Phaidra is building the future of industrial automation. The world today is filled with static, monolithic infrastructure. Factories, power plants, buildings, etc. operate the same they've operated for decades — because the controls programming is hard-coded. Thousands of lines of rules and heuristics that define how the machines interact with each other. The result of all this hard-coding is that facilities are frozen in time, unable to adapt to their environment while their performance slowly degrades. Phaidra creates AI-powered control systems for the industrial sector, enabling industrial facilities to automatically learn and improve over time. Specifically:We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data into high-value actions and decisions.We focus on industrial applications, which tend to be well-sensorized with measurable KPIs — perfect for reinforcement learning.We enable domain experts (our users) to configure the AI control systems (i.e. agents) without writing code. They define what they want their AI agents to do, and we do it for them. Our team has a track record of applying AI to some of the toughest problems. From achieving superhuman performance with DeepMind's AlphaGo, to reducing the energy required to cool Google's Data Centers by 40%, we deeply understand AI and how to apply it in production for massive impact. Phaidra's ability to achieve its mission is determined by our ability to work together — as defined by our core values: Transparency, Collaboration, Operational Excellence, Ownership, and Empathy. We seek individuals who embody these values, as they are instrumental in ensuring our team consistently delivers excellence and fosters an engaging and supportive culture Phaidra is based in the USA, but we are 100% remote with no physical office. We hire employees internationally with the help of our partner, OysterHR. Our team is currently located throughout the USA, Canada, UK, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India.The Opportunity At Phaidra, security is the bedrock of trust for our customers operating the world's most critical infrastructure. We are looking for a Senior Product Security Engineer to partner directly with our Agentic AI department. This team is at the forefront of our mission, building autonomous agents responsible for optimizing the operational fabric of AI factories. These agents don't just chat; they act. They make real-time decisions to optimize power usage, cooling efficiency, and hardware health, creating a more stable and efficient environment for massive-scale compute. This is a high-stakes environment where the integrity and security of AI-driven decisions are paramount. You will tackle the unique security challenges of deploying autonomous agents that interact directly with physical control systems. Security failures here don't just mean data leaks; they could mean operational downtime or physical degradation of critical hardware. We need a security expert who thrives working hand-in-hand with AI researchers and engineers. Your role is to embed security into the DNA of our Agentic platform, ensuring that as our agents learn and explore, they do so within unbreakable safety boundaries. We are seeking a team member located in the following areas: UK or US (central and eastern time preferred)ResponsibilitiesChampion Secure Agentic AI Development: Drive the adoption of Phaidra's Secure AI/ML Development Lifecycle (SAIDL) within the Agentic AI team. Adapt security practices to fit the iterative and experimental nature of Reinforcement Learning and agent development.Agentic Threat Modeling: Partner with researchers to model threats specific to autonomous agents. Beyond standard AI risks, you will analyze risks unique to agents, such as goal misalignment, reward hacking, infinite looping, and insecure tool execution (e.g., an agent executing a command that exceeds safety limits).Secure Agent Architecture & Safety Boundaries: Design secure-by-default architectures for autonomous agents. Crucially, this involves defining deterministic safety guardrails that sit between the probabilistic AI model and the physical hardware controls. Ensure "Zero Trust" applies to the agent—it should only have the minimum permissions needed to adjust specific parameters.Secure Agent Tools & Memory: Architect security controls for the "tools" the agent uses (APIs to read sensors or change settings) and the agent's long-term memory. Ensure the agent cannot be manipulated into using a tool to perform unauthorized actions or "poisoned" via its memory context.MLSecOps for RL Pipelines: Secure the training and simulation pipelines used for Reinforcement Learning. Ensure the integrity of the simulation environments (Digital Twins) used to train agents, preventing attackers from influencing agent behavior during the training phase.Adversarial Testing & Red Teaming: Lead AI Red Teaming exercises focused on behavioral manipulation. Can you trick the agent into making a suboptimal decision? Can you manipulate the observations the agent receives?Incident Preparedness: Develop incident response playbooks tailored for autonomous systems, focusing on "Kill Switches" and rapid rollback capabilities in the event of rogue agent behavior.Cross-Functional Partnership: Build strong relationships with the Agentic AI researchers, SREs, and Data Scientists. Act as an enabler who helps them deploy powerful agents safely, rather than a blocker.Key QualificationsAgentic AI & RL Security: Proven understanding of the security risks associated with Reinforcement Learning, Autonomous Agents, or automated decision-making systems.AI Partnership: Demonstrated experience working embedded with AI system developers and researchers. You understand the difference between "probabilistic" (AI) and "deterministic" (Code) and how to secure the bridge between them.Core Experience: 5+ years of work experience in product security, application security, or a closely related security engineering role.Safety Engineering Mindset: You understand that in physical systems, "Availability" and "Safety" often outrank "Confidentiality." You are familiar with concepts like fail-safes and human-in-the-loop controls.Technical Depth:Strong programming experience, ideally with Python (essential for ML/AI ecosystems) or Go.Familiarity with agent frameworks (e.g., LangChain, AutoGPT) or RL libraries (e.g., Ray RLLib).Proven experience securing Cloud infrastructure (GCP) and Kubernetes.Deep understanding of Authentication & Authorization (specifically non-human identities/workload identity).Advanced MLOps: Direct, hands-on experience securing MLOps tooling (e.g., Kubeflow, MLflow) and deep understanding of securing complex data and model-training pipelines.Preferred Skills & ExperienceIndustrial / OT Context: Experience working with systems that interface with the physical world (IoT, Robotics, ICS/OT). Understanding of the "IT/OT convergence."Formal Verification for AI: Experience using mathematical methods to prove that an AI model or agent will not violate specific safety constraints.Sim-to-Real Security: Experience securing simulation environments (Digital Twins) and managing the security risks of transferring policies from simulation to the real world.Protocol Fuzzing: Ability to test industrial protocols (e.g., Modbus, BACnet) for robustness against automated or adversarial inputs.AI Governance: Familiarity with emerging standards like the NIST AI RMF or ISO 42001.Critical Systems: Experience securing "closed loops" or control systems where latency and reliability are critical.Certifications: Relevant advanced certifications, such as GICSP (Global Industrial Cyber Security Professional), ISA/IEC 62443 Cybersecurity Expert, NVIDIA Agentic AI, OSEP (Offensive Security Experienced Penetration Tester), CISSP, or OSCP.Our StackAI/ML: PyTorch, TensorFlow, Ray (RL), LangChain, Gemini/OpenAI/Anthropic models.Languages: Python, Go.Infrastructure: Docker, Kubernetes, Terraform.Cloud: GCP (GKE, PubSub, BigTable)Onboarding In your first 30 days — (Foundation and AI Landscape Familiarization)Understand the Mission: Deep dive into "Building AI for AI Factories." Understand the specific physical parameters (power, cooling, airflow) our agents are optimizing.Build Trust: Sit with the Agentic AI researchers to understand their workflow. How do they train agents? How do they simulate environments?Initial Review: Conduct a high-level review of the current "Safety Layer" that sits between the Agent and the control systems. In your first 60 days — (Threat Modeling & Guardrails):Agent Threat Model: Lead a detailed threat modeling session for a specific Agentic workflow. Focus on the Interface between the Agent and the Physical Hardware.Guardrail Implementation: Propose and begin implementing technical controls (guardrails) that enforce deterministic safety rules, ensuring the AI cannot exceed operational limits regardless of its intent.Secure the Tools: Review the security of the internal APIs (Tools) that the agents use to sense and act on the environment. In your first 90 days — (Strategy & Automation):Reference Architecture: Publish a "Secure Agent Reference Architecture" for future agent development.Driving Initiatives: Drive the implementation of the secure reference architectures and remediation of key findings from the threat modeling exercises.Demonstrable Impact: Showcase measurable improvements in the security of the AI/ML pipeline (e.g., implementation of runtime monitoring for anomalous model behavior, reduction of AI-specific vulnerabilities).Strategic Contributions: Establish yourself as the key security partner and expert for the Agentic AI Department.General Interview Process All of our interviews are held via Google Meet, and an active camera connection is required.Meeting with People Operations team member (30 minutes)Meeting with Hiring Manager (30 minutes)Technical Interview with our Senior Product Security Engineer (60 minutes)Meeting with Agentic AI team member (45 minutes)Culture fit interview with Phaidra's co-founders (30 minutes)Base Salary US Residents:Tier 1 (Largest highest-cost metros): 146,200 USD - 199,600 USDTier 2 (Other major metros): 138,640 USD - 189,370 USDTier 3 (Mid-sized metro areas): 131,080 USD - 179,140 USDTier 4 (All other locations): 123,520 USD - 168,910 USD UK Residents:Tier 1 (London): 95,200 GBP - 142,000 GBPTier 2 (Manchester, Birmingham, Edinburgh, Bristol): 89,600 GBP - 134,400 GBPTier 3 (Other areas): 84,000 GPB - 126,000 GBP In addition to base salary, this position is eligible for equity. Final salary will be determined based on several factors, including a candidate's qualifications, skills, competencies, experience, expertise, education and location. In some cases, final compensation may fall outside the posted range. Salary ranges are regularly reviewed and may be adjusted in response to market trends. Benefits & PerksFast-paced, team-oriented environment where your work directly shapes the company's direction.We are a 100% remote company. Competitive compensation & meaningful equity.Outsized responsibilities & professional development.Training is foundational; functional, customer immersion, and development training.Medical, dental, and vision insurance (exact benefits vary by region).Unlimited paid time off, with a required minimum of 20 days per year.Paid parental leave (exact benefits vary by region).Flexible stipends to support your workspace, well-being, and continued professional development.Company MacBook. Please note: Not all of Phaidra's benefits and perks listed above apply to temporary employees such as interns.On being Remote We take a thoughtful and intentional approach to remote collaboration. Inspired by pioneers like GitLab, we embrace proven best practices to foster an exceptional remote work environment. Our culture is documentation-first, and we prioritize asynchronous communication to support focus and flexibility across time zones. While we value independence, we stay closely connected through tools like Slack and video conferencing. Weekly all-hands meetings help us align and build strong relationships, and we regularly host virtual team-building activities and social events to maintain a sense of camaraderie.Equal Opportunity Employment Phaidra is an Equal Opportunity Employer; employment with Phaidra is governed on the basis of merit, competence, and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status. We welcome diversity and strive to maintain an inclusive environment for all employees. If you need assistance with completing the application process, please contact us at Notice Phaidra participates in E-Verify, an employment authorization database provided through the U.S. Department of Homeland Security (DHS) and Social Security Administration (SSA). As required by law, we will provide the SSA and, if necessary, the DHS, with information from each new employee's Form I-9 to confirm work authorization for those residing in the United States. Additional information about E-Verify can be found here. #LI-Remote To be considered for any position at Phaidra, you must submit an online application. This role will remain open until it is filled. Phaidra only hires individuals who are legally authorized to work in the specified location(s) above. We do not provide employment sponsorship. Candidates requiring visa sponsorship, either now or in the future, are not eligible for hire. WE DO NOT ACCEPT APPLICATIONS FROM RECRUITERS.

Job Description About Us Daat Lab is an AI venture studio that collaborates directly with businesses to transform deep domain expertise into valuable, commercial-grade AI products. We assist companies in identifying and validating high-impact, AI-feasible use cases. Our process spans from initial ideation and building a proof of concept to developing a fully commercializable product and successfully launching it in the market. We maintain an active presence across multiple sectors, including real estate, digital marketing/e-commerce, and health, with an ambitious goal of adding one new product to our portfolio every month. About the Role We are looking for a software engineer to help take concepts from ideas to product. You will work directly with our strategy team and business partners to identify high-value, AI-feasible opportunities, shape product direction, and execute quickly. You’ll own the full product cycle including: discovery, prototyping, building, shipping, and iteration, often across multiple projects at once. This is a hands-on role for someone who thrives in ambiguity, moves fast, and likes being close to customers. What You’ll Do: • Own multiple projects from end to end: brainstorming product concepts, generating POCs to be evaluated and owning builds to reach a minimum viable product., • Build full-stack applications (backend, frontend, data layer, infra, deployment, and monitoring)., • Experiment with AI functionality on each case: classical ML when needed (forecasting, scoring, classification), plus LLM/agent workflows and evaluation loops, • Establish engineering standards: code quality, CI/CD hygiene, logging/analytics, basic security, • You should have:, • 3+ years shipping full-stack products end-to-end, • Strong Python and modern web stack (React/Next.js + APIs + DB + deployments), • Data processing and Machine learning fundamentals (pandas, Sklearn, pytorch/tensorflow, model evaluation), • Experience experimenting with LLMs and Agent frameworks (Open AI SDK, Google ADK, Langchain/Langraph, MCP), • Comfort with ambiguity, fast cycles, and customer-facing iteration, • What Success Looks Like:, • Ship multiple POCs that clearly validate feasibility and ROI, • Salary: $110 - 140k (full-time hire, will start as a 1099 contractor) Please note: Daat Lab is a separate entity, unrelated to Alchemy Worx.

Job Description About the Role: We are in search of an exceptional Machine Learning Engineer to join our accomplished team. In this role, you will take the lead in developing and fine-tuning predictive ML models, with a primary focus on Ad Score and Ad Account Health. You will play a crucial part in delivering actionable insights and solutions to our clients, and your work will be integral to our mission. Responsibilities include but are not limited to; • ML Model Development: Lead the development and refinement of predictive ML models, particularly Ad Score and Ad Account Health., • Data Analysis: Conduct in-depth data analysis to identify trends, patterns, and insights that inform model development and optimization., • Feature Engineering: Collaborate with data engineers to create and maintain feature engineering pipelines to support model training., • Model Evaluation: Implement rigorous evaluation methodologies to assess model performance, making necessary adjustments for continuous improvement., • Deployment and Integration: Work closely with engineering teams to deploy models and integrate them into our products through APIs., • Collaboration: Collaborate closely with product managers, full-stack engineers, and TPMs to ensure seamless integration of data science solutions into our products., • Master's or Ph.D. in a related field with a strong academic background., • Proven experience as a Data Scientist with a track record of developing and deploying predictive ML models., • Expertise in machine learning techniques, including but not limited to regression, classification, clustering, and deep learning., • Proficiency in data manipulation, feature engineering, and model evaluation., • Strong programming skills in languages such as Python and experience with libraries like TensorFlow, PyTorch, or scikit-learn., • Excellent communication skills and the ability to collaborate effectively within cross-functional teams., • A passion for continuous learning and staying updated with the latest trends and technologies in data science., • Strong problem-solving abilities and the capacity to translate complex data into actionable insights. Required knowledge of: • Python, • SQL, • Cloud Platforms (GCP, AWS, Azure), • Data Warehouses (BigQuery, Snowflake, Redshift), • LLMs / AI APIs, • Git / GitHubNice to have:, • Data Transformation (dbt), • Semantic Layers (Cube, Looker, dbt Metrics), • TypeScript, • Bayesian modeling experience - ideally Marketing Mix Models (PyMC, Stan, or similar..). Understands priors, MCMC sampling, posterior diagnostics., • Causal inference / experimentation- geo experiments (matched markets), A/B testing at scale. Familiar with incrementality measurement., • Marketing/advertising domain- understanding of attribution, media channels (paid social, search, display, video), campaign structures., • Nice to have - familiarity with adstock/saturation curves and budget optimization Our interview process includes, but is not limited to the following: • Unlimited vacation policy, • Monthly Phone Stipend, • Comprehensive Medical, Dental, and Vision insurance options, • 401(K) plan with matching, • Dog friendly office, • Hybrid work opportunity, • Professional Development Program Level 1 - Possesses essential capabilities * $145,057-$175,256 * Level 2 - Possesses developing capabilities * $175,256-$205,456 * Level 3 - Possesses notable capabilities. * $205,456-$235,655 * Level 4 - Possesses strong capabilities. * $235,655-$265,855 * Level 5 - Possesses advanced capabilities. * * $265,855-$296,054 About WITHIN & Brkfst.io WITHIN is the world's first Performance Branding company, partnering with some of the biggest brands in the world to drive business growth through innovative marketing strategies. Our integrated operating model collapses the traditional marketing silos between creative and media, performance and brand, and across media channels. With a full suite of offerings including media, creative, SEO, Lifecycle, Retail Media, Affiliate and Influencer, we're able to work with our brand partners in an integrated fashion, allowing us to align marketing strategies back to core business objectives. Client teams at WITHIN are trained on how to always act as a trusted business partner, acting as a fiduciary to client needs above our own. Teams at WITHIN have the ability to work with iconic brands such as The North Face, Timberland, Ben and Jerry's and Jose Cuervo. Everyone at WITHIN wants to grow and be challenged. It's a collaborative place made up of small, closely knit and versatile teams that are fast and adaptive to solve problems and build systems. Check out some of our work! Brkfst.io is an incubated venture within WITHIN, focused on connecting creators with major brands to produce impactful ads at scale. We are, and we connect creators with big brands to make impactful ads at scale. We have a network of thousands of content creators with a variety of different skill-sets, and we're looking to build a program that empowers them to create great ads for brands like Corkcicle, Perfume.com, Casper Mattresses, & more! This is a very ambitious project and our roadmap is full of exciting features, so we're looking for people who love innovation and want to be challenged. We're a small team of scrappy individuals who are growing fast, and we're looking for someone to grow with us. We weave AI into everything we do, using the latest tech across all teams to innovate, work smarter, and make better decisions. Whether it's in creative, operations, or anything else, AI helps us level up and do things at a whole new scale. We expect our people to use AI in their daily work, fully embracing it as a critical tool to help us succeed. Join Our Network! Stay connected with us and be the first to know about new opportunities, industry insights, and updates. Follow us on: • LinkedIn, • WhatsApp Community, • Instagram, • New York City: 43-01 22nd St, Suite 602, Queens, NY 11101, United States, • Bogotá: WeWork Av. Carrera 19 #100-45 Usaquén, Piso (Floor) 10, Bogotá, Distrito Capital de Bogotá 110111, Colombia, • Mexico City: Av. Paseo de la Reforma 296, Piso (Floor) 25, Oficina 111, Ciudad de México, CDMX 06500, México

Job DescriptionCompany Description At Fun.xyz, we believe a tokenized future is a beneficial inevitability, granting financial emancipation to everyone with an internet connection. For a blockchain-first global economy to exist, value exchange must become intuitive, secure, and seamless. Enter Checkout — the highest converting payment solution in web3. By integrating Checkout, dApps enable users to complete any on-chain action using crypto from a wallet, centralized exchange, or on-ramped fiat from a card or bank account. With access to market-leading routing and their asset portfolio at the point of sale, users are faster, stickier, and more engaged. For dApps, Checkout scales their user acquisition potential without launching new chains or integrating multiple third party providers. We built Checkout because we envision a world where anyone, anywhere, can purchase anything with any asset. If that sounds like a vision you can get behind, we want to hear from you. Role Overview We’re looking for a Data Scientist to supercharge our data-driven strategy and ensure we’re building the right things at the right time. You’ll be embedded in the heart of our product and engineering teams, shaping insights that guide strategy, uncover user behavior, and optimize execution across Fun.xyz. This role is perfect for someone who thrives in fast-paced, ownership-driven environments, loves solving open-ended problems, and is excited about the potential of blockchain data.Responsibilities • Design and build scalable data models to analyze transaction flows and user behavior, • Support the optimization of data transformations, pipelines, and workflows, • Work directly with the CFO to provide insights on the business’ performance, • Maintain data quality, observability, and reproducibility across analytical systems, • Partner with engineers and product teams to deliver actionable, production-grade insights, • Run experiments, apply causal inference, and drive evidence-based product decisions, • Translate complex data into clear stories for both technical and non-technical stakeholdersRequirements, • Proficiency in Python and SQL (5+ years), • Strong background in statistical analysis, experimentation, and causal inference, • Familiar with analyzing end-user behavior for optimization (A/B testing, conversion rates, etc.), • Skilled in modern data platforms (Redshift, Snowflake, Databricks, etc.) and orchestration/transformation tools (dbt or similar), • Deep curiosity about crypto, DeFi, on-chain data, and Web3 infrastructureNice To Have, • Experience analyzing large-scale transaction or financial datasets, • Hands-on experience with ML/AI libraries (Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow), • Familiarity with market dynamics (spreads, slippage, liquidity, etc.), • A track record of thriving in ownership-heavy, fast-moving environments

Job DescriptionCoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at . What You'll Do The Field Engineering team at Weights & Biases plays a vital role in ensuring customer success and adoption of our platform. As part of this team, we partner with Sales, Support, Product, and Engineering to lead technical success after the sales process. We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs). About the Role We're hiring an AI Solutions Engineer, Post-Sales to help customers solve real-world problems by enabling them to implement and scale ML pipelines and agentic workflows using Weights & Biases. In this role, you'll collaborate with engineering teams to ensure smooth onboarding and adoption, act as a trusted advisor on best practices, and represent the voice of the customer internally. You will partner directly with leading AI teams to optimize workflows, share technical expertise, and influence our product roadmap based on real-world customer feedback. This is an ideal opportunity for ML practitioners who are customer-focused and eager to work with top AI companies globally. Who You Are • 3–5 years of relevant experience in a similar role, • Strong programming proficiency in Python, • Hands-on experience enabling production-grade ML systems, with a focus on training and inference pipelines, experiment tracking, deployment patterns, and observability using deep learning frameworks (TensorFlow/Keras, PyTorch/PyTorch Lightning) and MLOps tooling (e.g. Airflow, Kubeflow, Ray, TensorRT)., • Familiarity with cloud platforms (AWS, GCP, Azure), • Experience with GenAI/LLMs and related tools (e.g. LangChain/LangGraph, HuggingFace Transformers, Pinecone, Weaviate), • Strong experience with Linux/Unix, • Excellent communication and presentation skills, both written and verbal, • Ability to break down and solve complex problems through customer consultation and execution, • Background in robotics, • TypeScript experience, • Proficiency with Fastai, scikit-learn, XGBoost, or LightGBM, • Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes, • You love collaborating with top AI teams to solve real-world ML and GenAI challenges, • You're curious about how to optimize AI workflows at scale, • Be Curious at Your Core, • Act Like an Owner, • Empower Employees, • Deliver Best-in-Class Client Experiences The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). What We Offer The range we've posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location. In addition to a competitive salary, we offer a variety of benefits to support your needs, including: • Medical, dental, and vision insurance - 100% paid for by CoreWeave, • Company-paid Life Insurance, • Voluntary supplemental life insurance, • Short and long-term disability insurance, • Flexible Spending Account, • Health Savings Account, • Tuition Reimbursement, • Ability to Participate in Employee Stock Purchase Program (ESPP), • Mental Wellness Benefits through Spring Health, • Family-Forming support provided by Carrot, • Paid Parental Leave, • Flexible, full-service childcare support with Kinside, • 401(k) with a generous employer match, • Flexible PTO, • Catered lunch each day in our office and data center locations, • A casual work environment While we prioritize a hybrid work environment, remote work may be considered for candidates located more than 30 miles from an office, based on role requirements for specialized skill sets. New hires will be invited to attend onboarding at one of our hubs within their first month. Teams also gather quarterly to support collaboration. California Consumer Privacy Act - California applicants only CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information. As part of this commitment and consistent with the Americans with Disabilities Act (ADA), CoreWeave will ensure that qualified applicants and candidates with disabilities are provided reasonable accommodations for the hiring process, unless such accommodation would cause an undue hardship. If reasonable accommodation is needed, please contact: . Export Control Compliance This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. CoreWeave may, for legitimate business reasons, decline to pursue any export licensing process.

Job Description Machine Learning Engineer / AI Engineer Newark, NJ Salary: $120,000 – $180,000 Description / Position Overview This role owns the design, deployment, and long-term performance of production machine learning systems that power data-driven products and operational intelligence. The Machine Learning Engineer is responsible for translating business problems into scalable AI solutions. This includes building end-to-end pipelines, deploying models into production environments, and continuously improving model performance. This is an engineering execution role, not a research-only position. Success requires the ability to move models from experimentation to reliable production systems that drive measurable product and operational impact. Requirements • Experience building and deploying machine learning models in real-world production environments, • Strong Python programming capability and experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn, • Understanding of statistics, probability, and optimization techniques used in machine learning systems, • Experience working with large-scale structured and unstructured datasets, • Experience deploying and operating models on cloud platforms such as AWS, GCP, or Azure, • Ability to build and manage scalable data pipelines supporting ML workflows, • Strong collaboration skills working with product, engineering, and data teams Responsibilities Machine Learning Development • Design, train, and optimize machine learning models for real-world business applications, • Build end-to-end ML pipelines including data ingestion, preprocessing, training, and evaluation, • Select appropriate algorithms and modeling approaches based on problem context, • Continuously improve model performance through experimentation and iteration Production Deployment & Infrastructure • Deploy machine learning models into scalable production environments, • Build APIs and services that operationalize machine learning capabilities inside products and systems, • Manage the full model lifecycle including monitoring, retraining, and drift detection, • Ensure reliability, scalability, and maintainability of ML infrastructure Data & Engineering Execution • Work with structured and unstructured data sources to support model development, • Implement feature engineering strategies that improve model accuracy and reliability, • Collaborate with data engineering teams to maintain clean and scalable data infrastructure, • Optimize model performance and resource efficiency in production systems Cross-Functional Collaboration • Partner with product and engineering teams to translate business needs into machine learning solutions, • Communicate technical insights and model behavior to non-technical stakeholders, • Contribute to AI roadmap planning and prioritization of ML initiatives Must-Haves • Proven experience deploying machine learning models into production environments, • Strong Python and ML framework expertise (TensorFlow, PyTorch, or Scikit-learn), • Experience building scalable ML pipelines and APIs, • Experience working with cloud infrastructure (AWS, GCP, or Azure), • Strong understanding of statistical modeling and machine learning fundamentals Final Invitation to Apply If you are a hands-on machine learning engineer who thrives on building real-world AI systems and taking models from concept to production impact, this is an opportunity to own meaningful technology that drives product innovation and operational performance. Contact Email Resume: Apply Online: Refer a friend, get up to $1000!

Job DescriptionCompany Description Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds to pursue greater human understanding, pioneering discoveries, and service to society. The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through seventeen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good. Job Description Seeking analytics professionals to serve as a part-time Associate for a graduate-level course on Applied Generative AI. An Associate is a faculty line junior to a Lecturer, that provides subject matter expertise and supports the instructional process for a course section. Serving as an Associate is an outstanding way to gain exposure to graduate-level teaching at Columbia University. The Applied Generative AI course provides students with a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation. Responsibilities * Attend all class sessions, assist with instruction, lead breakout sessions, facilitate discussions. * Evaluate, grade student work and assessments as requested by the course Lecturer. * Monitor and address student concerns and inquiries. Qualifications Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting. Requirements * Graduate degree in an area related to Machine Learning, Computer Science, Applied Mathematics, or related field. * 3+ years of related applied professional experience. Preferred Skills & Experience * Programming experience in Python and experience with major deep learning frameworks such as PyTorch or TensorFlow. * Knowledge of deep learning architectures, such as CNNs, VAEs, GANs, and RNNs. * Experience with deploying code on cloud platforms such as AWS, GCP, or Azure. * Knowledge of Mathematics and Probability concepts used in machine learning, including * Optimization, Gradient Descent, Conditional Probability, Bayes Theorem, and Normal Distribution. Additional Information Please submit a resume inclusive of university teaching experience. All your information will be kept confidential according to EEO guidelines. Columbia University is an Equal Opportunity/Affirmative Action employer.

Job DescriptionCompany Description Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds in pursuit of greater human understanding, pioneering new discoveries, and service to society. The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through fourteen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good. Job Description The Applied Analytics program is seeking data analytics professionals to serve as a part-time Associate for a graduate-level course called Applied Deep Learning and AI. This advanced course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models. An Associate is a faculty line junior to a Lecturer, that provides subject matter expertise and supports the instructional process for a course section. Serving as an Associate is an outstanding way to gain exposure to graduate-level teaching at Columbia University. Responsibilities • Attend all on-campus class sessions, assist with instruction, lead breakout sessions, facilitate discussions., • Evaluate, grade student work and assessments as requested by the course Lecturer., • Monitor and address student concerns and inquiries, • Conduct office hours.Qualifications Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting. Requirements • Graduate degree in Computer Science, Data Science, or a related field (Completing relevant coursework such as Deep Learning, Machine Learning, and Statistics courses)., • Proficient in Python and familiar with deep learning frameworks (e.g., TensorFlow, PyTorch)., • Deep Learning Knowledge: Strong understanding of CNNs, RNNs, LLMs, Reinforcement Learning, and model evaluation techniques., • Hands-on experience with deep learning projects and data manipulation., • Strong verbal and written skills for explaining concepts clearly., • University teaching experience in related subjects Additional Information Please submit a resume inclusive of university teaching experience. All your information will be kept confidential according to EEO guidelines. Columbia University is an Equal Opportunity/Affirmative Action employer.

Job DescriptionAbout Fusemachines Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.Salary Range: US$ 140,000-190,000/yearRole Overview We’re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You’ll work across the ML lifecycle—from problem framing and data exploration to model development, evaluation, deployment, and monitoring—often in partnership with client stakeholders and internal delivery teams. You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems. Key Responsibilities • Problem Framing & Stakeholder Partnership, • Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)., • Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)., • Data Analysis & Feature Engineering, • Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses., • Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices., • Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions., • Model Development (Core ML), • Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)., • Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation., • Build time series models (statistical and ML/DL approaches) and validate with proper backtesting., • Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness., • Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making., • Deep Learning, • Build and train deep learning models using PyTorch or TensorFlow/Keras., • Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)., • Evaluation, Explainability, and Iteration, • Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports., • Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence., • Productionization & MLOps (Project-Dependent), • Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration., • Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans., • Documentation & Communication, • Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders., • Create documentation and lightweight demos that make results actionable.Success in This Role Looks Like, • You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)., • Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring., • Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly.Required Qualifications, • 3–8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)., • Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)., • Strong SQL skills (joins, window functions, aggregation, performance awareness)., • Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset., • Hands-on experience across multiple model types, including:, • Classification & regression, • Time series forecasting, • Clustering/segmentation, • Experience with deep learning in PyTorch or TensorFlow/Keras., • Strong problem-solving skills: ability to work with ambiguous goals and messy data., • Clear communication skills and ability to translate analysis into decisions.Preferred Qualifications, • Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)., • Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)., • Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)., • Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)., • Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)., • Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks), • Databricks certifications (Data Scientist, Data Engineer, or related)Nice-to-Have, • Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)., • Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products., • Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability.Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws. Important: Immigration Sponsorship Policy Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including: • Direct Company Sponsorship: Such as H-1B, J-1, or TN visas., • Employer of Record: Listing Fusemachines as the immigration employer on any government documentation. I0GDC3XJwj

Job DescriptionAbout Fusemachines Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government. Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI. About the Role A Lead Data Scientist is responsible for designing and implementing data-driven solutions to complex business problems. The role requires extensive experience in data analysis, agentic AI, statistical modeling, machine learning, and data visualization, as well as the ability to lead a team of data scientists and collaborate with cross-functional teams. Responsibilities • Team Leadership: Lead a team of data scientists to develop innovative solutions to complex business problems. Mentor and develop the skills of junior data scientists and provide feedback and guidance to help them improve their work., • Collaboration: Collaborate with cross-functional teams, including business stakeholders, product managers, software engineers, and data engineers to develop and implement data-driven solutions., • Strategic Assessment: Assess the business needs of clients and identify areas where AI can be used to improve processes, reduce costs, or increase revenue., • Solution Design: Design and implement statistical models, machine learning algorithms, predictive analytics models, and agentic systems to solve business problems., • Communication: Communicate technical insights and recommendations to non-technical stakeholders in a clear and concise manner., • Continuous Learning: Stay up-to-date with the latest developments in data science, machine learning, and artificial intelligence, and apply new technologies and techniques to solve business problems., • Pipeline Management: Responsible for developing, implementing, and managing end-to-end machine learning pipelines. This will involve building, deploying, and maintaining machine learning models, as well as ensuring data quality and system stability. Requirements for the Role, • Education: Bachelor's, Master's, PhD, or advanced training in applied mathematics, engineering, computer science, or a similar related field., • Experience: 6+ years of total experience in Data Science, Machine Learning & Generative AI., • Cloud Computing: 4+ years of hands-on experience with AWS, including deep expertise in deploying models and managing compute environments., • IBM WatsonX: 2+ years of hands-on experience with IBM WatsonX, • Agentic AI Tools: Experience with LangGraph, Google ADK or similar., • Agentic Architectures : Experience with advanced RAG & multi-agent systems., • Programming: Strong programming skills in languages such as Python, R, C++, and SQL., • Frameworks: Hands-on experience with ML frameworks, such as PyTorch or TensorFlow. XDOyWllq0r

Job DescriptionCompany Description On-Site 1yr contract $10950/month About the Role We are seeking a Senior Generative AI Engineer to design, build, and deploy production-grade AI applications powered by large language models (LLMs). In this role, you will lead the end-to-end development of Generative AI solutions, including LLM-powered applications, retrieval-augmented generation (RAG) systems, agentic workflows, model evaluation pipelines, and production infrastructure. You will work cross-functionally with product, finance, data, and business stakeholders to translate real-world business problems into scalable AI systems that deliver measurable value. Job Description • Design and develop algorithms for generative models using deep learning techniques, • Design and build LLM-powered applications for internal and/or customer-facing use cases, • Develop and productionize RAG pipelines using enterprise data sources, vector databases, and retrieval systems, • Build and optimize AI agents / agentic workflows for task automation, reasoning, and orchestration, • Integrate model providers such as OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and open-source models where appropriate, • Create robust evaluation frameworks for response quality, factuality, latency, safety, and reliability, • Implement prompt engineering, structured outputs, tool calling, and model optimization strategies, • Deploy scalable AI services to cloud environments using modern software engineering and MLOps practices, • Build monitoring, observability, and feedback loops for model and application performance in production, • Establish and maintain guardrails, responsible AI practices, and security controls for enterprise AI systems, • Collaborate with product managers, designers, and business stakeholders to identify high-impact AI opportunities, • Mentor other engineers and contribute to architecture, technical direction, and engineering best practices Qualifications • Bachelor’s degree in Computer Science, Engineering, Machine Learning, or a related field, • 5+ years of software engineering, machine/deep learning engineering, or applied AI experience, • 2+ years of hands-on experience building and deploying Generative AI / LLM-based systems in production, • Strong programming skills in Python and experience with backend/API development, • Experience with LLM application development, including prompt engineering, RAG, tool use, and structured output design, • Experience in optimizing RAG pipelines using both structured and unstructured data, • Experience with orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent, • Experience in generative AI techniques such as GANs, and VAEs, • Hands-on experience with vector databases / retrieval systems such as Pinecone, Weaviate, Chroma, FAISS, Elasticsearch, or Azure AI Search, • Experience with cloud platforms such as AWS, GCP, or Azure, • Experience with Docker, Kubernetes, CI/CD, and production deployment practices, • Strong understanding of software architecture, scalability, reliability, and distributed systems, • Experience building evaluation, testing, and monitoring for AI systems, • Strong communication skills and ability to work closely with technical and non-technical stakeholder Preferred Qualifications • Experience fine-tuning or adapting open-source LLMs, • Advanced knowledge of natural language processing for text generation tasks, • Experience with PyTorch, TensorFlow, JAX, or related ML frameworks, • Experience with MLOps tools such as MLflow, SageMaker, Vertex AI, Azure ML, Kubeflow, or similar, • Experience building multi-agent systems or advanced orchestration workflows, • Experience with AI safety, guardrails, red-teaming, privacy, and governance, • Familiarity with search, ranking, recommendation, conversational AI, or enterprise knowledge systems, • Experience in customer-facing or enterprise SaaS products, • Experience in semiconductor/manufacturing, retail and e-commerce sectors Additional Information All your information will be kept confidential according to EEO guidelines.

Job DescriptionTagup is a defense technology company founded at MIT that is redefining logistics superiority with next-generation AI. We’re growing rapidly and are looking for change-makers passionate about delivering innovative technologies to solve the most challenging problems in the world’s highest stakes environments. This is an exciting opportunity to engage in meaningful work that strengthens national security and contributes to the success of U.S. and allied forces. Join us in shaping the future of defense logistics for a safer tomorrow. We are seeking a Machine Learning Engineer to join our Salt Lake City team and help shape the next generation of AI-driven defense and aviation systems. In this role, you’ll go beyond building models — you’ll design, deploy, and scale AI solutions that directly support mission-critical operations. This is not a typical ML position. You’ll work at the intersection of cutting-edge research and real-world application, creating models and infrastructure that deliver measurable improvements in reliability, efficiency, and performance. If you’re motivated by solving complex technical challenges and want your work to make a tangible impact on national security and aviation safety, we want to hear from you.What You’ll Do • Develop, train, and optimize ML models for large-scale applications., • Build pipelines for data ingestion and model deployment., • Work with engineers and subject-matter experts to refine solutions., • Conduct testing and validation to ensure reliability., • Co-author technical reports on data analysis and model performance., • Continuously improve ML infrastructure and workflows., • Collaborate with customers to identify new data sources and the industrial processes they will support; some customer travel may be required.What We’re Looking For, • 4+ years of machine learning experience, with strong Python skills and proficiency in frameworks such as PyTorch or TensorFlow., • Proven ability to deploy ML models into production and work with large, complex datasets., • Hands-on experience with MLOps tools and practices, including Kubernetes, MLflow, and CI/CD pipelines., • Experience building and managing cloud infrastructure as code (AWS, Azure, or GCP) with tools such as Terraform or Ansible.

Job Description Location: US Remote Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on behalf of a high-growth AI cybersecurity company backed by top-tier investors and strategic AI leaders. The company is building the security layer for the AI era, protecting enterprises against AI-powered threats such as deepfakes, smishing, and synthetic voice attacks. Following a major Series B funding round led by industry-leading AI and venture partners, the company is entering a critical growth phase. Trusted by major banks, technology companies, and healthcare organizations, they are scaling rapidly to meet enterprise demand in a $200B+ market opportunity. We are looking for a Staff Machine Learning Engineer to define and build the companys ML capabilities from the ground up. ML is central to the product vision. This role is not about incremental optimization — it is about owning the strategy, infrastructure, and execution of machine learning across the organization. There is currently no dedicated ML infrastructure or ML team. You will establish the foundations: define where ML drives product value, design production systems end-to-end, and set the technical direction for how ML evolves across the company. This is a high-impact, highly autonomous role suited for someone who has built ML systems in production and is ready to architect an ML function from zero to scale. What Youll Do * Define the companys ML strategy: where ML should be applied across products, what infrastructure is required, and how to approach build vs. buy decisions. * Design and build production ML systems end-to-end — including data pipelines, model training workflows, evaluation frameworks, and inference serving. * Establish rigorous evaluation methodology to measure model quality, detect regressions, and support data-driven iteration. * Own the data strategy: determine what data is needed, how it should be labeled, how feedback loops are structured, and how models continuously improve. * Partner closely with product and backend engineers to integrate ML into customer-facing systems. * Write production-quality code within the existing codebase and contribute to architectural decisions. * Over time, help recruit, mentor, and lead the ML team as the function expands. What Were Looking For * 8+ years of experience building ML systems in production environments. * Experience standing up ML infrastructure at an early-stage startup or serving as the senior/lead ML engineer at a company. * Strong software engineering fundamentals with production experience in languages such as Python, Java, or TypeScript. * Experience with cloud-based ML infrastructure (e.g., SageMaker, Bedrock, Modal, Baseten, or similar platforms). * Hands-on experience with ML and data frameworks such as PyTorch, TensorFlow, Spark, or equivalent tools. * Comfortable working across the stack — infrastructure, backend systems, and data platforms. * Demonstrated ability to mentor engineers and elevate technical standards within a team. * High autonomy and ownership mindset, with the ability to define direction and execute without predefined playbooks. Bonus Points * Experience building ML systems for security, fraud detection, or adversarial environments. * Experience working with LLMs in production (evaluation, fine-tuning, retrieval systems, guardrails). * Background in real-time inference systems or high-throughput distributed systems. * Experience making strategic build vs. buy infrastructure decisions. * Previous startup experience in high-growth environments. Why This Role Will Pivot Your Career * Strategic AI backing: Supported by leading AI and venture investors shaping the future of AI infrastructure and cybersecurity. * Founding-level impact: Build and define the ML function from zero in a company where ML is core to product value. * Enterprise traction: Products already trusted by major banks, tech firms, and healthcare organizations. * Massive market opportunity: Positioned in a rapidly expanding AI cybersecurity space. * Leadership path: Opportunity to evolve into Head of ML as the organization scales. * Ownership & autonomy: Direct influence over architecture, infrastructure, and long-term technical direction. Benefits * Competitive salary & equity options * Health, Dental, and Vision * 401k * Hybrid flexibility Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Job Description Basic Qualifications: • Knowledge of different compilation steps (and ability to fix) in languages such as Go, C, C++, or Java., • Excellent communication skills and the ability to collaborate effectively with cross-functional teams, • Ability to mentor and grow a group of other Software Engineers, • Ability to lead projects with several cross-org dependencies and design complexities, • Large-scale training using data structures and algorithms, • Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning) Preferred Qualifications • PhD or equivalent in Computer Science, Engineering, Mathematics or related field, • 8+ years of experience in professional software development experience, developing and deploying systems in production environments, • Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib, • Excellent communication skills and the ability to collaborate effectively with cross-functional teams, • Ability to mentor and grow a group of other Software Engineers, • Anomaly Detection Techniques, • Deep Learning, • Scalable ML architecture, • Feature management, • Causal ML, • Reinforcement learning Anticipated base 250-280k + bonus + equity and comprehensive benefits