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About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace the___ to do your best work. Creating a career you love? It's Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process ___. We are looking for a Staff Data Scientist for our Engagement Ecosystem. You will shape the future of people-facing and business-facing products we build at Pinterest. Your expertise in quantitative modeling, experimentation and algorithms will be utilized to solve some of the most complex engineering challenges at the company. You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Design, Research, Product Analytics, Data Engineering and others. The results of your work will influence and uplevel our product development teams while introducing greater scientific rigor into the real world products serving hundreds of millions of pinners, creators, advertisers and merchants around the world. What you'll do • Develop a deep, nuanced understanding of the Pinterest engagement ecosystem and key product surfaces, quantifying ecosystem-level opportunities and risks., • Lead projects on:, • Tradeoffs between organic engagement and advertising., • Deep dives on how engagement metrics impact monetization and retention., • Understanding and predicting the value of core behaviors (e.g., saving, repinning, board creation) as they relate to downstream business outcomes., • Designing and evaluating interventions that sustainably boost enterprise metrics across product boundaries., • Design and productionize robust, scalable ML and evaluation frameworks-spanning forecasting, recommendation, and causal inference., • Advocate for best-in-class experimentation, instrumentation, and metric design; bridge the gap between short-term proxy metrics and long-term business impact., • Collaborate across disciplines-Product, Engineering, Research, Business, and Design-translating complex data questions into actionable business insights., • Mentor and guide junior and senior scientists, fostering intellectual curiosity and driving technical excellence., • Use advanced ML, causal inference, and generative AI techniques to model and explain complex interactions across the Pinterest ecosystem (e.g., organic engagement, ads, content quality, monetization), turning highly ambiguous business questions into clear, testable hypotheses and decision frameworks for Core and Monetization leadership., • Apply AI-assisted analysis and developer tools (e.g., copilot, intelligent dashboards) to accelerate exploration, improve code quality, and scale insight generation, ensuring we can quickly evaluate tradeoffs like ad load vs. Pinner engagement., • Champion responsible and scientifically rigorous use of AI across the ecosystem, setting best practices for instrumentation, experimentation, and metric design so that AI-driven changes balance engagement, revenue, and long-term user trust. What we're looking for • 10+ years of hands-on experience in web-scale data environments, with a track record of solving hard, ambiguous problems in product, engagement, or ecosystem analytics., • Deep expertise in: Machine Learning (recommendation, ranking, prediction, experimentation), Statistical Modeling & Causal Inference (observational and experimental data), Product analytics/strategy (beyond dashboards: root cause, goaling, design collaboration), Programming in Python/R and advanced SQL/Spark., • Strong product intuition-ability to scope, question, and design the right solutions for ill-defined, high-impact business problems., • Scientific rigor and healthy skepticism: You challenge assumptions, find flaws, and drive towards robust, reproducible outcomes., • Exceptional communication: You make the complex simple, and can influence both technical and non-technical audiences., • Track record mentoring and growing data talent at the staff/senior IC level., • Cross-functional leadership and the ability to align competing interests towards shared goals., • Proven track record applying machine learning and/or causal inference to complex, ambiguous product or marketplace problems at scale (e.g., ads, recommendations, multi-sided ecosystems), from problem framing and experimental design through to influencing senior stakeholders and driving product or policy decisions., • Hands-on experience with modern AI/ML tools and workflows (such as PyTorch, TensorFlow, scikit-learn, or similar, plus LLM-based assistants or copilots) and comfort rapidly adopting new AI capabilities into day-to-day analysis, prototyping, and experimentation workflows., • Masters degree in a technical field (e.g., Computer Science, Statistics, Mathematics, Engineering, Social Sciences). In-Office Requirement Statement: • We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection., • This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country. Relocation Statement: • This position is not eligible for relocation assistance. Visit our___ page to learn more about our working model. #LI-NM4 At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found ___. US based applicants only$164,695—$339,078 USD Our Commitment to Inclusion: Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete___for support.

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 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 investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world. The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research and resources that serve all of our enterprise clients. As an ML Sys Research Engineer, you'll work on building out the algorithms for our next-gen Agent RL training platform, support large scale training, and research and integrate state-of-the-art technologies to optimize our ML system. Your customer will be other MLREs and AAIs on the Enterprise AI team who are taking the training algorithms and applying them to client use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models. If you are excited about shaping the future of the modern AI movement, we would love to hear from you! You will: • Build, profile and optimize our training and inference framework., • Post-train state of the art models, developed both internally and from the community, to define stable post-training recipes for our enterprise engagements., • Collaborate with ML teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.., • At least 1-3 years of LLM training in a production environment, • Passionate about system optimization, • Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc., • Ability to demonstrate know-how on how to operate the architecture of the modern GPU cluster, • Experience with multi-node LLM training and inference, • Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc., • Strong written and verbal communication skills to operate in a cross functional team environment. 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 Pay Transparency provision. 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.

Focuses on the organization, management, and archiving of research data for a variety of studies, projects, and initiatives. Supports research programs by ensuring data integrity, accessibility, and compliance with institutional guidelines for secure data handling and storage. Assists with the development and implementation of data management protocols, maintains detailed documentation of data workflows, and collaborates with research team members to ensure proper data organization and version control. Provides support for data queries, tracking, and updates to facilitate efficient project progression and reporting. The Research Assistant is ideally suited for recent university graduates who want to gain research experience and strengthen their preparation for MD or PhD studies after earning a Bachelor's or Master's degree. The anticipated term for this position is one year. The primary focus of this role is supporting multicenter pediatric trauma research by organizing, archiving, and managing large multimodal datasets, as well as contributing to analytical studies related to machine learning, clinical decision rules, and time-to-intervention evaluations. Responsibilities include curating, organizing, and archiving research data across multiple studies; maintaining meticulous version control and data documentation; collaborating closely with research fellows, data scientists, and faculty on dataset development and preparation for analysis; assisting with data exploration, quality control, and troubleshooting; synthesizing literature related to trauma, clinical decision rules, and diagnostic imaging; and preparing research reports, abstracts, and figures for presentations and publications. This role offers the opportunity to develop essential research and data management skills using real-world clinical datasets, with exposure to advanced computational tools and professional development through collaboration with multidisciplinary teams. Candidates with interests in clinical research, pediatric emergency medicine, computer vision, or data science are highly encouraged to apply. The ideal candidate is self-directed, detail-oriented, and an effective communicator in both written and spoken English. Prior research experience is a plus but not required. Required Qualifications: • Bachelor's degree in computer science, data science or related area and / or equivalent experience / training., • Basic skills to analyze and research information and learn to synthesize large amounts of data with exquisite attention to detail., • Basic program and project management skills., • Ability to closely monitor computing resource usage and associated costs, ensuring the project stays within budget, and provide regular spending updates., • Ability to meet precise deadlines and be part of a fast-paced problem-solving team that works with minimum supervision and is extremely motivated, • Technical proficiency in standard Microsoft Office Software and ability to learn UCSF computational resources including but not limited to Research Analysis Environment and Scientific Computing Services for secure data hosting and computing services, • High integrity and strong work ethic, • Skills to learn organization-specific and other computer application programs., • Basic communication and interpersonal skills to communicate effectively, both verbally and in writing., • Ability to use discretion and maintain all confidentiality. Ability to use sound judgment in responding to issues and concerns. Preferred Qualifications: • Analytical abilities and familiarity with programming tools including: Python (including Pandas, Sci-kit Learn, OpenCV, PyTorch), R, SQL, HTML/CSS, C, STATA, • Coursework in Machine Learning, Algorithms/Advanced Data Structures, Data Science, Probability, Data Graphics and Visualizations, Modern Regression, Technical Writing, High Dimensional Inference Required Qualifications: • Bachelor's degree in computer science, data science or related area and / or equivalent experience / training., • Basic skills to analyze and research information and learn to synthesize large amounts of data with exquisite attention to detail., • Basic program and project management skills., • Ability to closely monitor computing resource usage and associated costs, ensuring the project stays within budget, and provide regular spending updates., • Ability to meet precise deadlines and be part of a fast-paced problem-solving team that works with minimum supervision and is extremely motivated, • Technical proficiency in standard Microsoft Office Software and ability to learn UCSF computational resources including but not limited to Research Analysis Environment and Scientific Computing Services for secure data hosting and computing services, • High integrity and strong work ethic, • Skills to learn organization-specific and other computer application programs., • Basic communication and interpersonal skills to communicate effectively, both verbally and in writing., • Ability to use discretion and maintain all confidentiality. Ability to use sound judgment in responding to issues and concerns. Preferred Qualifications: • Analytical abilities and familiarity with programming tools including: Python (including Pandas, Sci-kit Learn, OpenCV, PyTorch), R, SQL, HTML/CSS, C, STATA, • Coursework in Machine Learning, Algorithms/Advanced Data Structures, Data Science, Probability, Data Graphics and Visualizations, Modern Regression, Technical Writing, High Dimensional Inference, • Familiarity with software: MS Office, Tableau, Figma, Smartsheets, MD.Ai, REDCap

Job Summary: The NeuroAI Lab at UCSF, led by Assistant Professor Pedro Pinheiro-Chagas, invites applications for a Data Scientist to support and advance our research initiatives at the intersection of AI and cognitive neuroscience. The successful candidate will contribute to a multidisciplinary team developing cutting-edge AI systems for the discovery, diagnosis, and care of neurodegenerative diseases. The Data Scientist will work across the lab's core research areas, including: (1) foundation AI models that leverage large-scale clinical databases to identify disease subtypes and risk factors; (2) agentic AI decision support systems that synthesize multimodal patient data for specialist-level diagnoses; and (3) AI conversational agents for scalable clinical assessments. The role involves designing and maintaining robust data pipelines, curating and analyzing complex multimodal datasets (clinical notes, neuropsychological assessments, neuroimaging), developing and evaluating machine learning models, and contributing to research publications. The ideal candidate will have strong programming skills and experience with generative AI and traditional ML algorithms, a passion for translational research, and the ability to work effectively across disciplines. This position offers exceptional opportunities for professional growth at the forefront of health AI, with direct exposure to world-class clinical collaborators and the potential to impact real-world patient care. Department Description: The NeuroAI Lab operates at the ___. We are also affiliated with the ___. % of time Essential Function (Yes/No) Key Responsibilities (To be completed by Supervisor) 20% Plans long-term statistical studies, including the preparation of proposals, design of survey instruments and determining sampling procedures. 20% Gathers, analyzes, prepares and summarizes the collection of information and data; recommends statistical approaches, trends, sources and uses. 5% Prepares data for presentation. 10% Identifies multivariate strategies. 5% Prepares reports of studies for internal validation and cross validation studies. 5% Analyzes the interrelationships of data and defines logical aspects of data sets. 10% Develops systems for organizing data to analyze, identify and report trends. 10% Manages database of research data for projects. 5% Reviews new software instruments and potential effects on statistical testing. May make programming modifications. 5% Participates in development and implementation of data security policies and procedures. 5% Keeps abreast of technical advances in storage, documentation and dissemination of computerized data. 0 0 0 0 100% (To update total %, enter the amount of time in whole numbers (without the % symbol - e.g., 15, 20) then highlight the total sum (e.g., 1%) at the bottom of the column and press F9. The total sum should add up to 100%.) Required Qualifications: • Bachelor's degree in related area and / or equivalent experience / training., • Proficiency in Python, with demonstrated experience in data analysis, machine learning, and statistical modeling., • Experience building and maintaining data pipelines, integrating APIs, and working with large-scale datasets., • Familiarity with version control (Git) and collaborative software development practices., • Strong analytical and problem-solving skills, with the ability to work independently and manage multiple projects., • Excellent written and verbal communication skills. Preferred Qualifications: • Master's or Ph.D. in Computer Science, Data Science, Biomedical Informatics, Computational Neuroscience, Biostatistics, or a related quantitative field., • Experience with LLMs., • Familiarity with clinical, cognitive, or health-related datasets (e.g., electronic health records, neuroimaging, neuropsychological assessments)., • Knowledge of data security, privacy, and regulatory standards for clinical applications (e.g., HIPAA compliance)., • Experience with deep learning frameworks (PyTorch, TensorFlow) and cloud computing platforms., • Track record of peer-reviewed publications or contributions to open-source projects., • Interest in or experience with agentic AI architectures, retrieval-augmented generation (RAG), or multi-agent systems. Required Qualifications: • Bachelor's degree in related area and / or equivalent experience / training., • Proficiency in Python, with demonstrated experience in data analysis, machine learning, and statistical modeling., • Experience building and maintaining data pipelines, integrating APIs, and working with large-scale datasets., • Familiarity with version control (Git) and collaborative software development practices., • Strong analytical and problem-solving skills, with the ability to work independently and manage multiple projects., • Excellent written and verbal communication skills. Preferred Qualifications: • Master's or Ph.D. in Computer Science, Data Science, Biomedical Informatics, Computational Neuroscience, Biostatistics, or a related quantitative field., • Experience with LLMs., • Familiarity with clinical, cognitive, or health-related datasets (e.g., electronic health records, neuroimaging, neuropsychological assessments)., • Knowledge of data security, privacy, and regulatory standards for clinical applications (e.g., HIPAA compliance)., • Experience with deep learning frameworks (PyTorch, TensorFlow) and cloud computing platforms., • Track record of peer-reviewed publications or contributions to open-source projects., • Interest in or experience with agentic AI architectures, retrieval-augmented generation (RAG), or multi-agent systems., • Ability to thrive in a fast-paced, multidisciplinary research environment.

Job Summary: This position requires 1 day onsite per week, based on business needs, this may increase. UCSF Health is seeking a highly skilled AI Engineer to design, build, and deploy scalable AI-driven applications that improve clinical operations, patient care, and health system efficiency. This role spans the full AI lifecycle, including data pipeline development, model training and evaluation, and deployment of machine learning and generative AI solutions on modern cloud platforms. The AI Engineer will translate emerging technologies, including large language models (LLMs), into production-ready tools that integrate with healthcare systems. Responsibilities include building robust data pipelines, deploying machine learning and generative AI models, and developing APIs or web-based applications that enable seamless use in clinical and operational workflows. This role works closely with clinicians, data scientists, and IT teams to deliver solutions that are reliable, secure, and scalable. Competitive applicants for this position are software, data, or machine learning engineers with 5+ years of experience building AI systems in production. They have strong skills in Python and SQL, and hands-on experience with MLOps, CI/CD, and cloud platforms (e.g., GCP, AWS, or Azure). Candidates should be comfortable developing and maintaining data pipelines, deploying machine learning or generative AI models, and building APIs or lightweight web applications, with the ability to work across the stack and deliver practical, user-facing solutions in a healthcare environment. Department Overview: The Health AI team is part of the larger UCSF Health IT team and supports the development, implementation, and monitoring of artificial intelligence, machine learning, and other analytical tools, improving patient care, clinician experience, and health system operations. The team's expertise spans data science, machine learning, software/data engineering, business, nursing informatics, and medical informatics. UCSF Health IT provides clinical and informatics leadership to achieve the strategic priorities of UCSF Health, as enabled by technology. Health IT is a multidisciplinary team of clinicians and technology professionals that partners with clinical, business, and technology leaders throughout the organization, using information technology to improve the quality, safety, and value of care, and the patient and provider experience, for UCSF and its partner organizations. % of time Essential Function (Yes/No) Key Responsibilities (To be completed by Supervisor) 10 Y Applies advanced software concepts to plan, design, develop, modify, debug, deploy and evaluate highly complex software for functional areas. Analyzes existing highly complex software or works to formulate logic and devises algorithms for new highly complex software systems. Performs highly complex data analysis and tests / debugs highly complex software, working directly with management. Initiates, analyzes, designs and applies highly complex data sources. Applies and enforces complex programming security practices. 10 Y Specifies, develops and executes complex test plans. Develops conversion and system implementation plans. Performs or directs highly complex data modeling, performance and integration testing and builds interfaces. Determines source code control techniques and configuration management design and changes. 5 Y Prepares and approves or obtains approval for system and programming documentation. Initiates and oversees changes in development, maintenance and system standards. Sets the technical requirements for complex software specifications. 5 Y Understands and applies industry practices, community standards and department policies and procedures in depth. May serve as technical lead for multiple software development projects of moderate to broad scope. May lead a team of software development professionals. Enforces project plans. 15 Y Build and maintain data integration (with SQL databases or APIs) and data processing and transformation pipelines to support the development and implementation of AI/ML tools 15 Y Identify and build systems for implementation, monitoring, and maintenance of AI/ML tools using modern MLOps practices including CI/CD, model versioning, and cloud-native deployment. 5 Y Collaborate with data scientists and researchers to design and implement highly complex metrics and processes to automatically monitor AI/ML tools for safety, potential bias or drift, performance, and validity. 10 Design and develop APIs, services, or lightweight web applications to enable integration of AI/ML and generative AI capabilities into clinical and operational workflows. 15 Develop and deploy generative AI solutions (e.g., LLM-based systems), including prompt engineering, retrieval-augmented generation (RAG), and evaluation frameworks for unstructured data. 10 Design and implement highly complex real-time or near real-time data processing and inference systems to support AI/ML and generative AI applications. Develop scalable, event-driven architectures and ensure reliable, low-latency integration of AI capabilities into production clinical and operational workflows 100% (To update total %, enter the amount of time in whole numbers (without the % symbol - e.g., 15, 20) then highlight the total sum (e.g., 1%) at the bottom of the column and press F9. The total sum should add up to 100%.) Required Qualifications: • Bachelor's degree in related area and / or equivalent experience / training., • 5 years of experience in positions of increasing responsibility designing, implementing, and maintaining complex AI/ML applications., • Experience with data analysis and machine learning tools such as Jupyter, Pandas, scikit-learn, Numpy/Scipy, PyTorch, etc., • Demonstrated advanced knowledge of full software development lifecycle, • Advanced experience with Python; ability to write clean, efficient, and production-level Python code, • Advanced experience with SQL (e.g., SQLServer, PostgreSQL), • Demonstrated experience deploying, monitoring, and maintaining AI/ML models and pipelines, • Experience designing and developing APIs or microservices to support AI/ML applications, • Familiarity with web application development frameworks (e.g., React, JavaScript/TypeScript) or integrating backend systems with user-facing applications, • Experience with large language models (LLMs), including prompt engineering, evaluation, and production deployment, • Experience with real-time or streaming data processing systems and low-latency inference architectures, • Demonstrated effective communication and interpersonal skills, • Demonstrated ability to communicate technical information to technical and non-technical personnel at various levels in the organization, • Self-motivated and works independently and as part of a team. Able to learn effectively and meet deadlines, • Demonstrated broad problem-solving skills, • Demonstrated ability to interface with management on a regular basis, • Excellent project leadership and management skills. Preferred Qualifications: • Master's degree or PhD in Computer Science, Computer Engineering, or related area and/or equivalent experience/training., • Epic Clarity or Clinical Data Model, • Familiar with data visualization tools (e.g., Tableau) Required Qualifications: • Bachelor's degree in related area and / or equivalent experience / training., • 5 years of experience in positions of increasing responsibility designing, implementing, and maintaining complex AI/ML applications., • Experience with data analysis and machine learning tools such as Jupyter, Pandas, scikit-learn, Numpy/Scipy, PyTorch, etc., • Demonstrated advanced knowledge of full software development lifecycle, • Advanced experience with Python; ability to write clean, efficient, and production-level Python code, • Advanced experience with SQL (e.g., SQLServer, PostgreSQL), • Demonstrated experience deploying, monitoring, and maintaining AI/ML models and pipelines, • Experience designing and developing APIs or microservices to support AI/ML applications, • Familiarity with web application development frameworks (e.g., React, JavaScript/TypeScript) or integrating backend systems with user-facing applications, • Experience with large language models (LLMs), including prompt engineering, evaluation, and production deployment, • Experience with real-time or streaming data processing systems and low-latency inference architectures, • Demonstrated effective communication and interpersonal skills, • Demonstrated ability to communicate technical information to technical and non-technical personnel at various levels in the organization, • Self-motivated and works independently and as part of a team. Able to learn effectively and meet deadlines, • Demonstrated broad problem-solving skills, • Demonstrated ability to interface with management on a regular basis, • Excellent project leadership and management skills. Preferred Qualifications: • Master's degree or PhD in Computer Science, Computer Engineering, or related area and/or equivalent experience/training., • Epic Clarity or Clinical Data Model, • Familiar with data visualization tools (e.g., Tableau), • Experience with Epic data structures

About Cleerly We’re Cleerly – a healthcare company that’s revolutionizing how heart disease is diagnosed, treated, and tracked. We were founded in 2017 by one of the world’s leading cardiologists and are a growing team of world-class engineering, operations, medical affairs, marketing, and sales leaders. We raised $223M in Series C funding in 2022 which has enabled rapid growth and continued support of our mission. In December 2024 we received an additional $106M in a Series C extension funding. Most of our teams work remotely and have access to our offices in Denver, Colorado, New, York, New York, Dallas, Texas, and Lisbon, Portugal with some roles requiring you to be on-site in a location. Cleerly has created a new standard of care for heart disease through value-based, AI-driven precision diagnostic solutions with the goal of helping prevent heart attacks. Our technology goes beyond traditional measures of heart disease by enabling comprehensive quantification and characterization of atherosclerosis, or plaque buildup, in each of the heart arteries. Cleerly’s solutions are supported by more than a decade of performing some of the world’s largest clinical trials to identify important findings beyond symptoms that increase a person’s risk of heart attacks. At Cleerly, we collaborate digitally and use a wide variety of systems. Our people use Google Workspace (GMail, Drive, Docs, Sheets, Slides), Slack, Confluence/Jira, and Zoom Video, prior experience in these areas is a plus. Role or department specific technology needs may vary and will be listed as requirements in the job description. While we are mostly a remote company, travel is required for some team meetings and cross function projects typically once per month or once per quarter, for some roles like sales or external facing roles travel could be up to 90% of the time. About the Opportunity We're seeking a Senior Software Engineer to drive key technical initiatives across our computational imaging pipeline. In this high-impact role, you'll own the complex systems responsible for deploying, scaling, and monitoring our regulated AI algorithms end-to-end. You will influence the architecture of our system, ensuring our solutions meet medical standards for quality and performance, while mentoring other engineers and helping shape our technical culture. About the Team You will join our expert Computational Imaging team, where we develop sophisticated algorithms to enhance and improve medical imaging. We are focused on improving the quality and clinical value of medical images, creating powerful, extra software components that correct artifacts, standardize data, and ensure the utmost precision before the final analysis. Our work is essential for elevating the reliability and accuracy of Cleerly’s regulated products. Responsibilities • Design, build, and deploy scalable AI services and computational imaging pipelines to production, ensuring robust, high-availability infrastructure for our machine learning algorithms., • Design extensible system architectures and make pragmatic trade-offs that balance performance, security, and maintainability., • Own full lifecycle delivery of complex features, from architectural planning and API design to implementation and post-release observability., • Ensure high availability and observability of our services; improve alerting, logging, and incident response across the stack., • Set and uphold strong engineering standards via code reviews, technical mentorship, and design documentation., • Lead technical design reviews and system decomposition efforts across teams., • Proactively identify risks and gaps in operational or architectural resilience, and drive durable improvements., • Collaborate closely across AI and engineering to reduce knowledge silos and ensure continuity in critical systems through cross-training and documentation., • Design and implement robust testing mechanism and validation strategies to ensure compliance with our Quality Management System and regulatory standards, • Contribute to the necessary technical documentation required for regulatory submissions. Requirements • Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field (or equivalent experience)., • 8–12 years of software engineering experience, with expertise in AI production systems (Python, PyTorch) and data services (SQL, Postgres, NoSQL, Redis or similar)., • Demonstrable capability in designing, implementing, and securing RESTful web services, • Experience in MLOps orchestration and cloud deployment (Docker, Kubernetes)., • Experience with AWS, GitHub, and continuous integration pipelines, • Proven track record of architecting and delivering scalable systems in production environments., • Experience mentoring engineers and raising the technical bar through reviews and design feedback., • Strong systems thinking with the ability to evaluate tradeoffs in scalability, reliability, and maintainability., • Experience designing for observability and operational excellence (e.g., logs, alerts, dashboards, runbooks)., • Comfortable operating in ambiguity and driving clarity across product, engineering, and business stakeholders. Preferred: • Experience designing and optimizing end-to-end medical imaging pipelines in a production environment and familiarity with HIPAA/HITRUST security requirements., • Hands-on experience with Computer Vision and Deep Learning techniques used for medical image processing. OUR COMPANY IS AN EQUAL OPPORTUNITY EMPLOYER. We do not discriminate on the basis of race, color, national origin, ancestry, citizenship status, protected veteran status, religion, physical or mental disability, marital status, sex, sexual orientation, gender identity or expression, age, or any other basis protected by law, ordinance, or regulation. Job duties, activities, and responsibilities are subject to change by our company. The base salary range for this role varies by location and is aligned to market benchmarks. • Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range., • Final compensation is determined based on location, experience, skills, and internal equity. This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges: • Base Salary: $153,000 - $179,000, • TTC: $176,000 - $206,000 *Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards. Working at Cleerly takes HEART. Discover our Core Values: • H: Humility- be a servant leader, • E: Excellence- deliver world-changing results, • A: Accountability- do what you say; expect the same from others, • R: Remarkable- inspire & innovate with impact, • T: Teamwork- together we win Don’t meet 100 percent of the qualifications? Apply anyway and help us diversify our candidate pool and workforce. We value experience, whether gained formally or informally on the job or through other experiences. Job duties, activities and responsibilities are subject to change by our company. OUR COMPANY IS AN EQUAL OPPORTUNITY EMPLOYER. We do not discriminate on the basis of race, color, national origin, ancestry, citizenship status, protected veteran status, religion, physical or mental disability, marital status, sex, sexual orientation, gender identity or expression, age, or any other basis protected by law, ordinance, or regulation. For more information see our Privacy Policy (___. All official emails will come from @cleerlyhealth.com email accounts. #Cleerly

Location Our Senior Cloud Architect, ML/AI will be an integral part of our global Forward Deployment Engineering team. This role is based remotely in the US, Colombia, Mexico, Canada, the UK, Ireland, Estonia, Sweden, the Netherlands, and Israel. The job is also open to contractors in Eastern Europe or Portugal. About DoiT DoiT is a global technology company that works with cloud-driven organizations to leverage public cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well-architected and scalable state—from planning to production. Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency. With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongside more than 4,000 customers worldwide. The Opportunity As a Senior Cloud Architect, ML/AI, you will be part of our global Forward Deployed Engineering organization, working with rapidly growing companies in EMEA and around the world. Depending on business needs, this role may be aligned to either Field Engineering (pre-sales + GTM) or FDE Delivery (install base, product adoption, customer health), with a common technical bar and shared expectations. You will: • Lead the design and implementation of production-grade ML and Generative AI solutions on AWS (with awareness of multi-cloud environments)., • Act as a hands-on expert and trusted advisor for customers running AI/ML workloads at scale, from initial discovery through deployment and optimization., • Translate complex business problems into cloud architectures that are secure, reliable, cost-efficient, and observable., • Help evolve how DoiT uses AI/ML internally and with customers by turning one-off solutions into reusable patterns and “gravel roads” that influence the product roadmap. For Field Engineering, you will focus more on pre-sales, POVs, CloudBuild engagements, and partner-led growth motions. For Delivery, you will focus more on install base health, product adoption, proactive engagements, and account-team work. • Lead discovery, architecture, and implementation for advanced ML and Generative AI workloads on AWS, including data, training, inference, and integration layers., • Own the technical success of your engagements: clearly define outcomes, make tradeoffs visible, and ensure designs are production-ready (security, reliability, performance, cost)., • Provide opinionated guidance on GenAI architectures (e.g., Amazon Bedrock, SageMaker, Q) and how they integrate with customers’ existing systems and processes. For Field Engineering: • Partner with Account Executives, Solution Engineers, and Growth FDEs to shape and win opportunities across all four GTM pillars in-region (product adoption, new logo acquisition, install base expansion, partner-led growth)., • Serve as technical lead for extended POVs and CloudBuild engagements focused on AI/ML and GenAI, demonstrating clear value and de-risking customer adoption., • Build compelling technical narratives and demos that support revenue-generating motions, including co-sell initiatives with CSP partners. For Delivery: • Act as a named technical advisor for a portfolio of existing customers, working within account teams (Account Manager, CSM, FDE) to improve install base health and outcomes for AI/ML and GenAI workloads., • Lead proactive “Get FDE”–style engagements where AI/ML expertise is needed to unblock customers, reduce risk, or improve the impact of DoiT Cloud Intelligence., • Participate in structured account-team routines (e.g., objective setting, quarterly environment reviews) to keep AI/ML architectures aligned with customer goals and product adoption opportunities., • Recommend and implement AI/ML-related capabilities in DoiT Cloud Intelligence (e.g., CloudFlow, Insights, DataHub) as part of your customer engagements., • Document and measure the business and technical impact of your work, tying AI/ML initiatives to clear customer outcomes (cost, performance, reliability, productivity). For Field Engineering: • Design and run service-led product adoption plays that use AI/ML and GenAI projects to drive deeper adoption of DoiT’s platforms, in partnership with Growth leadership., • Ensure AI/ML-focused CloudBuild and POV engagements include mandatory product adoption playbooks, with clear activation and follow-up criteria. For Delivery: • Execute against delivery programs and automations that detect product struggles (e.g., customers failing to complete AI/ML workflows, incomplete CloudFlow pipelines) and turn those into targeted AI/ML advisory engagements., • Use every relevant delivery touchpoint to recommend and operationalize product adoption (e.g., Insights, automation, FinOps/CloudOps workflows) in collaboration with account teams., • Maintain a high personal bar for delivery quality: clear scopes, realistic plans, strong communication, and crisp technical documentation., • Capture repeatable AI/ML patterns, reference architectures, and runbooks that other engineers can apply across customers. For Field Engineering: • Identify and validate “gravel road” solutions—custom AI/ML or GenAI integrations and patterns that should be elevated into standard offerings or product features., • Work with Product, R&D, and growth leaders to submit and champion these patterns into the roadmap, connecting field innovation to scalable packages and revenue engines. For Delivery: • Contribute to the FDE Delivery vision by turning recurring AI/ML implementation work into structured “gravel roads” (e.g., reusable CloudFlow patterns, Insights definitions, data pipelines) that can be productized., • Collaborate with FDE advocates and product teams to ensure field-built AI/ML solutions are vetted, documented, and, when appropriate, handed off for productization., • Collaborate closely with Sales, Customer Success, Product, and Business Systems Engineering to ensure AI/ML work is visible, repeatable, and connected to company priorities., • Communicate clearly with both technical and non-technical stakeholders, setting expectations and making risks and tradeoffs explicit. For Field Engineering: • Work with cloud partner teams (especially AWS) to align AI/ML initiatives to program funding, strategic bets, and co-sell motions—without compromising customer outcomes., • Provide technical leadership for partner-led opportunities involving GenAI and ML on AWS, ensuring DoiT’s value and DoiT Cloud Intelligence are central to the solution. For Delivery: • Coordinate with Account Managers, CSMs, TAMs, and other FDEs to ensure AI/ML engagements are sequenced correctly within broader account plans and install-base priorities., • Feed structured, field-derived feedback on product adoption barriers (especially for AI/ML capabilities) into Delivery leadership and product teams., • Use and maintain the systems, templates, and workflows that support planning, observability, and quality across Customer Experience (e.g., JIRA, documentation standards, dashboards)., • Contribute to internal enablement: teach other Doers about new AI/ML capabilities, share patterns, and help raise the bar globally for ML/AI expertise. For Field Engineering: • Ensure your AI/ML work is accurately reflected in pipelines, opportunities, and CloudBuild portfolios, enabling reliable reporting on technical win rates and influence on ARR., • Help improve forecast quality and POV coverage for AI/ML-related opportunities by maintaining good hygiene in the relevant systems. For Delivery: • Ensure AI/ML delivery engagements are logged, measured, and observable in the tools used for install-base health and product adoption tracking., • Participate in Delivery operating rhythms (e.g., team reviews, program updates) with clear, data-backed updates on your AI/ML work and impact., • Success metrics and specific objectives for this role will be defined and updated on an annual and quarterly basis through the company planning cycle, pod charters, Weekly Operating Review (WOR) scorecards, and relevant regional/functional scorecards., • As a Senior Cloud Architect, you are expected to:, • Understand the metrics and objectives that apply to your function (Field Engineering or FDE Delivery) and region., • Transparently report progress against those metrics., • Proactively propose and execute corrective actions when off track., • Experience, • 4+ years of experience architecting, deploying, and managing cloud-based AI/ML solutions, including production workloads., • Proven track record designing and operating large, distributed systems on AWS, selecting appropriate services and patterns to meet business and technical goals., • AWS & GenAI / ML Expertise, • Advanced proficiency with AWS services relevant to AI/ML and GenAI., • Hands-on experience with Amazon Bedrock for deploying and scaling foundation models and Generative AI workloads., • Experience fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker (including JumpStart)., • Strong prompt engineering skills and familiarity with rigorous model evaluation (quality, safety, performance)., • Understanding of agentic capabilities and patterns for AI agents that autonomously perform tasks and integrate with existing systems., • Experience with Amazon Q Business and Amazon Q Developer (or similar tools) to accelerate insight generation and development workflows., • ML Pipelines, Data & MLOps, • In-depth knowledge of Amazon SageMaker components such as Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability., • Proficiency integrating TensorFlow, PyTorch, and other ML frameworks with SageMaker for model development, fine-tuning, and deployment., • Experience with distributed training (multi-GPU or multi-node) and performance optimization for inference., • Strong data-engineering skills on AWS: Amazon S3, AWS Glue, Lake Formation, Redshift for AI/ML data pipelines., • Experience building end-to-end AI/ML workflows using services like AWS Lambda, Step Functions, API Gateway, and containerized deployments on Amazon EKS / AWS Fargate., • DevOps, MLOps, Governance & Security, • Hands-on experience with CI/CD for AI/ML using AWS CodePipeline, CodeBuild, SageMaker Pipelines, or similar., • Proficiency in monitoring and operating AI systems using Amazon CloudWatch and SageMaker Model Monitor., • Strong understanding of AI governance, security, and compliance on AWS, including IAM, KMS, and data privacy patterns., • Familiarity with AI ethics and bias detection/mitigation (e.g., using SageMaker Clarify or similar tools)., • Multi-Cloud Awareness & Collaboration, • Working knowledge of Google Cloud AI tools (e.g., Vertex AI, Cloud AutoML, BigQuery ML) sufficient to reason about multi-cloud architectures and integration points., • Proven ability to mentor peers, run enablement sessions, and collaborate across Sales, CS, and Product., • Soft Skills, • Excellent communication skills across technical and business audiences; able to simplify complex ideas and influence decisions., • Natural ownership mentality: you escalate early, resolve fast, and own the outcome., • Demonstrated ability to work effectively in a remote-first, global environment., • Education & Certifications, • BA/BS degree in Computer Science, Mathematics, or a related technical field, or equivalent practical experience., • Additional data or AI certifications (e.g., AWS/GCP data certifications, reputable AI/ML programs such as Stanford, Coursera, Udacity, MIT, eCornell)., • Expanded AI/ML & Dev Experience, • Experience with modern RLHF, advanced fine-tuning techniques, and hybrid AI architectures., • Familiarity with Hugging Face or similar open-source ecosystems integrated with AWS., • Prior experience as a ML Engineer, Data Scientist, or AI-focused Architect in a consulting or SaaS environment., • Tooling & Process, • Experience with JIRA or similar tools for tracking work across delivery and product-feedback cycles., • Exposure to Agile practices and frameworks commonly used for SaaS and cloud delivery. Are you a Do’er? Be your truest self. Work on your terms. Make a difference. We are home to a global team of incredible talent who work remotely and have the flexibility to have a schedule that balances your work and home life. We embrace and support leveling up your skills professionally and personally. What does being a Do’er mean? We’re all about being entrepreneurial, pursuing knowledge, and having fun! Click here to learn more about our ___. Sounds too good to be true? Check out our___. We thought so too, but we’re here and happy we hit that ‘apply’ button. Full-time employee benefits include: • Unlimited Vacation, • Flexible Working Options, • Health Insurance, • Parental Leave, • Employee Stock Option Plan, • Home Office Allowance, • Professional Development Stipend, • Peer Recognition Program Many Do’ers, One Team DoiT unites as Many Do’ers, One Team, where diversity is more than a goal—it's our strength. We actively cultivate an inclusive, equitable workplace, recognizing that each unique perspective enhances our innovation. By celebrating differences, we create an environment where every individual feels valued, contributing to our collective success. #LI-Remote

Role Number: 200662281-3401 Summary Are you a passionate Machine Learning Engineer with a deep love for photography? Join Apple's Camera Hardware Engineering team and help us redefine the camera experience for millions of users worldwide. As a key player in our innovative team, you will collaborate closely with hardware, software, and image processing specialists to develop cutting-edge camera technologies. Description As a Machine Learning Engineer in the Camera Hardware Engineering group you will be responsible for all research, design, development, test, and qualification of camera hardware for Apple products. This team is seeking an experienced Machine Learning Engineer with a background in Camera and image sensor technologies. You will bring your expertise to the team and be responsible for ongoing design, evaluation, benchmarking and characterization of Apple camera products. Minimum Qualifications * Bachelor's degree in Computer Science, Electrical Engineering, Physics, Optics, or a related field? * Experience with Python programming and deep learning frameworks like PyTorch? * Experience with machine learning and computer vision principles, and algorithms. * Experience with camera and/or image sensor technologies. Preferred Qualifications * MS or Ph.D. in Machine Learning, EE, CS, Physics, Optics, or equivalent and 3+ years of experience in machine learning research or relevant industry experience? * Experience with applying deep learning to various computer vision tasks, such as: Object recognition, Image segmentation, Inpainting, and Anomaly detection? * Experience in applying generative AI and reinforcement learning techniques to enhance hardware design processes. ? * Experience with using advanced ML methods to optimize system architectures and improve image quality in the hardware design domain. * Experience with image quality metrics and evaluation methodology? * Experience with camera components, functions, and the camera ISP pipeline * Experience with megapixel CMOS image sensor technology, lens selection, and qualification

About Baseten Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products. THE ROLE We’re looking for a customer-obsessed software engineer to come ship with us. You’ll own features like multi-node training and products like serverless reinforcement learning (RL) from conception to MVP (and from MVP to GA!). You’ll work through the stack, architecting solutions from API and UI down to our infrastructure layer. You’ll fine tune models yourself to develop an understanding of user workflows. You’ll work closely with research engineers leveraging state-of-the-art training techniques to build experiences that accelerate model development and solve for real pain points. If you’re excited to dive deep into the training, let’s talk! THE PRODUCT Take a look at what we’ve built so far: Overview of the product so far Training docs overview Story of the Training product Research we've done EXAMPLE INITIATIVES Checkpointing Pipeline: Our checkpointing pipeline starts with automated checkpointing, a feature that ensures that versions of models created during training are automatically backed up to the cloud. Users are able to then deploy checkpoints seamlessly into inference servers, providing point-and-click integrations into inference frameworks like vLLM and Baseten’s Inference Stack. This enables customers to quickly evaluate the performance of their checkpoints with real traffic. Multinode training: Multinode training enables customers to easily run training jobs across multiple compute nodes, enabling users to train large models like GLM 4.7 and DeepSeek. We’ve built deeply at the Kubernetes layer to ensure that scheduling, startup, inter-node communication, and shutdown happen seamlessly under the hood and as the user expects. Training DX: Customers come to train on Baseten because it helps them get to value fast. To do this, we ensure that the features we ship aren’t just fast, but are easy to iterate with. We enhanced Baseten’s metrics from pod-level GPU summaries to per-GPU and per-Node. We’ve built a CLI experience that caters to terminal users, and UI experiences that enable user to seamlessly manage their training jobs. Responsibilities Iterate like crazy Design ergonomic APIs and abstractions to model complex resources and lifecycles Work throughout the stack (API layer, backend and database implementation, infra layer; frontend is a plus) to implement features. Fine-tune and deploy models to develop intuition around training workflows. Partner closely with model developers and world-class research engineers to understand the requirements and pain points of post-training workflows. Drive long-term improvements to improve reliability of systems and velocity of development Fix bugs & resolve customer issues with urgency Requirements 5+ years experience building software applications Deep knowledge of the web stack, databases, and distributed systems Experience developing developer tooling or infrastructure products for external or internal users. Good taste in product, particularly developer-oriented tools Interest in ML/AI infrastructure and willingness to learn Driven by high agency and ownership Strong communication skills with the ability to bridge technical depth and business needs NICE TO HAVE Experience launching features and products through different release cycles (MVP, Beta, GA, etc.) Experience with model development methods and paradigms, like Supervised Fine-Tuning, Reinforcement Learning, Synthetic Data Generation, LoRA, Full Finetunes, etc. Familiarity or experience with the open source training stack and frameworks (NCCL, PyTorch, Megatron, NemoRL, VeRL, Axolotl, HF Trainer) and distributed training techniques (FSDP, DeepSpeed). Experience developing AI products, tooling, or agents Frontend fluency Benefits Competitive compensation, including meaningful equity. 100% coverage of medical, dental, and vision insurance for employee and dependents Generous PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Years Day!) Paid parental leave Company-facilitated 401(k) Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Compensation Range: $200K - $275K #J-18808-Ljbffr

Head of AI Type: Full-time Base Salary: $350,000 - $450,000 (Plus Bonus & Equity) Base Pay Range: $350,000.00/yr - $450,000.00/yr About the Role: Work with a company who are building AI systems with general physical ability — systems that do not merely analyze data, but actively explore, experiment, and engineer in the real world. The work spans robotics, machine learning, autonomous experimentation, and applied science, with a focus on enabling AI systems to generate new knowledge through physical interaction. We are seeking a Head of AI to lead the design and execution of our intelligence stack across autonomous agents, learning systems, and real‑world deployment. This is a senior, hands‑on leadership role for someone who can define long‑term AI strategy while remaining deeply involved in technical decision‑making. What You’ll Do: Own the end‑to‑end AI vision and roadmap, from foundational models to deployed autonomous systems. Define system architectures that integrate reinforcement learning, large models, perception, simulation, and physical feedback loops. Establish best practices for learning in data‑scarce, high‑cost, real‑world environments. Lead development of reinforcement learning systems for long‑horizon planning and control. Lead development of large models for reasoning, planning, and tool use. Lead development of autonomous agents capable of designing and executing physical experiments. Drive simulation‑to‑real transfer and ensure robustness in physical deployment. Partner closely with robotics, hardware, firmware, and manufacturing teams to tightly couple AI with physical systems. Build, mentor, and manage a team of senior AI researchers and engineers. Set a high technical bar while fostering a culture of rigorous experimentation and fast iteration. Translate ambiguous research goals into executable engineering plans. Applied Impact: Enable autonomous discovery across complex scientific and engineering domains. Ensure AI systems scale reliably and safely in real‑world operational settings. Represent the AI organization internally and externally as a technical leader. Who You Are: You are a deeply technical leader who enjoys working at the intersection of AI, robotics, and the physical sciences, and who is motivated by problems that do not have clean abstractions or easy datasets. Required Experience: 10+ years of experience in AI/ML research or engineering with significant hands‑on contributions. Deep expertise in one or more of: large language or foundation models; autonomous or robotic systems; proven experience deploying AI systems in real‑world, non‑simulated environments. Strong proficiency in Python and modern ML frameworks (e.g., PyTorch). Leadership & Approach: Experience leading senior, highly technical teams. Ability to balance long‑term vision with day‑to‑day technical execution. Strong scientific intuition and commitment to empirical validation. Why This Role: Build foundational AI systems that interact with and learn from the physical world. Shape the direction of autonomous experimentation and discovery. Work alongside a deeply interdisciplinary team tackling consequential real‑world problems. Influence the architecture and culture of a next‑generation AI organization. Seniority level Director Employment type Full‑time Job function Information Technology Industries Technology, Information and Media #J-18808-Ljbffr

Senior / Principal Machine Learning Scientist 2 days ago Be among the first 25 applicants Get AI-powered advice on this job and more exclusive features. Our Mission Our mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life. For more information, see our website at altoslabs.com. Our Value Our Single Altos Value: Everyone Owns Achieving Our Inspiring Mission . Diversity at Altos We believe that diverse perspectives are foundational to scientific innovation and inquiry. At Altos, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining a diverse and inclusive environment. What You Will Contribute To Altos As a Senior or Principal Machine Learning Scientist, you will play a prominent role in developing generative AI/ML models for multi‑modal, multiscale biology from virtual cells to agentic target assessment. We are looking for a hands‑on, creative, and collaborative individual to join our multidisciplinary team of scientists and engineers focused on transforming how we treat aging and disease. The successful candidate will thrive in a fast‑paced environment that emphasizes teamwork, transparency, scientific excellence, originality, rigor, and integrity. Responsibilities Pioneer novel machine learning methodologies and statistical frameworks (e.g., generative models, causal inference, diffusion models, and advanced transformer architectures) to address fundamental challenges in cell health and rejuvenation Contribute to setting the long‑term technical vision and research strategy for a core domain (e.g., multi‑modal data fusion, perturbation modeling) within the Institute of Computation Translate your deep understanding of the mathematical and theoretical underpinnings of cutting‑edge AI research into high‑impact applications Design, implement, and optimize large‑scale machine learning systems using modern frameworks (e.g., PyTorch, JAX) and agile practices Develop and manage efficient distributed training strategies across multiple GPUs and compute clusters to handle terabytes of multi‑modal biological data Develop robust approaches for multi‑modal data integration and cross‑domain mapping to extract actionable biological insights Apply computational thinking to solve problems in drug target identification, compound assessment, and prediction of cellular perturbation responses Lead the full ML development lifecycle from theoretical conception and data strategy through model development, training, and evaluation Act as a key technical mentor to Machine Learning Scientists and Engineers, raising the bar for scientific rigor and model robustness across the organization Who You Are Proven track record leveraging machine learning to solve real‑world problems Expertise in one or more of the following: generative models, language models, computer vision, Bayesian inference, causal reasoning & inference, transfer & multi‑task learning, diffusion models, graph neural networks, active learning, cooperative agents Experience writing production‑quality code with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or similar Experience with multi‑GPU and distributed training at scale A team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focused on professional growth Able to advise others across the wider function / company on cutting‑edge practices and approaches to enable the science / research. Desire to constantly expand your skillset and knowledge. Keen to learn more about biology, computational science, and medicine Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age‑related disabilities Minimum Qualifications Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Statistics, or a related quantitative field, demonstrating a deep theoretical foundation in ML/AI 6+ years of relevant post‑PhD work experience in either an academic or industry setting Proven experience developing and applying complex machine learning models, preferably with a significant portion of that time spent in a fast‑paced industry or translational research environment A strong track record of leading and publishing innovative, peer‑reviewed research in top‑tier ML conferences (e.g., NeurIPS, ICML, ICLR) or high‑impact scientific journals Excellent scientific communication skills: verbally and in writing; with computational and non‑computational audiences, in informal 1‑1 settings, team meetings, and formal seminars Expertise in several of the following: deep learning, reinforcement learning, generative models, language models, computer vision, Bayesian inference, causal reasoning & inference, transfer & multi‑task learning, graph neural networks, active learning, hybrid mechanistic/ML models Proven experience applying sophisticated ML techniques to molecular and cell biological data sets (e.g., NGS, spatial omics, bioimaging) Preferred Qualifications Experience in cell health and rejuvenation related research area Experience in the application of machine learning methods to biological data Experience in computational approaches to drug discovery Experience with software development methodologies and open‑source software Salary Ranges Redwood City, CA Senior Scientist I, Machine Learning: $280,500 - $379,500 Senior Scientist II, Machine Learning: $317,050 - $428,950 Principal Scientist, Machine Learning: $376,550 - $509,450 San Diego, CA Senior Scientist I, Machine Learning: $260,950 - $353,050 Senior Scientist II, Machine Learning: $285,600 - $386,400 Principal Scientist, Machine Learning: $340,000 - $460,000 Exact compensation may vary based on skills, experience, and location. Before Submitting Your Application Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice) This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment. Equal Opportunity Employment We value collaboration and scientific excellence. We believe that diverse perspectives and a culture of belonging are foundational to scientific innovation and inquiry. At Altos Labs, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining an inclusive environment. Altos Labs provides equal employment opportunities to all employees and applicants for employment, without regard to 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. Altos prohibits unlawful discrimination and harassment. 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. Thank you for your interest in Altos Labs where we strive for a culture of scientific excellence, learning, and belonging. Note: Altos Labs will not ask you to download a messaging app for an interview or outlay your own money to get started as an employee. If this sounds like your interaction with people claiming to be with Altos, it is not legitimate and has nothing to do with Altos. Learn more about a common job scam at Seniority level Mid‑Senior level Employment type Contract Job function Other Industries Biotechnology Research, Pharmaceutical Manufacturing, and Technology, Information and Internet #J-18808-Ljbffr

Brahma is conducting this search on behalf of our client. We're hiring across quantitative and machine learning roles and looking for strong talent at the intersection of rigorous analysis and applied impact. Whether your background skews toward research, engineering, or analytics, if you think in numbers and build with data, we want to talk. These are not cookie-cutter roles. You'll work on high-ambiguity problems, collaborate across disciplines, and be expected to go from exploratory analysis all the way through to deployed solutions. What You'll Do • Design and build machine learning models, statistical methods, and quantitative frameworks, • Work with large, complex datasets to surface actionable signals, • Run experiments, measure outcomes, and iterate rigorously, • Partner with engineering, product, and business stakeholders to drive decisions, • Communicate findings clearly to technical and non-technical audiences alike What We're Looking For • Advanced degree (MS or PhD) in a quantitative discipline — statistics, mathematics, CS, physics, operations research, or equivalent experience, • Strong foundations in probability, statistics, linear algebra, and optimization, • Proficiency in Python and relevant ML libraries (PyTorch, TensorFlow, scikit-learn, etc.), • Experience with SQL and large-scale data, • Ability to move fluidly between ambiguous research and production-quality work Nice to Have • Domain experience in [NLP / computer vision / time series / causal inference / reinforcement learning], • MLOps and cloud platform experience (AWS, GCP, Azure), • Published research, open-source contributions, or competition track record

OpenAI, Dall-e, Langchain, Retrieval Augmented Generation (RAG)) and experienced with ML packages like scikit-learn and PyTorch.

Job DescriptionML Engineer — Tilde Research Location: San Francisco, CA (Onsite) Compensation: $150,000 – $350,000 base + 0.5% – 1% equity About Tilde Research Tilde Research is a frontier AI research lab focused on mechanistic understanding of model architectures and optimization. They work on reasoning, pretraining, scaling laws, and architecture-level experimentation. Backed with strong funding and compute resources, Tilde operates with a small, highly selective team at the intersection of research and engineering. About the Role This is a research engineering role focused on implementing and scaling model development work at the frontier of AI. You will translate research ideas into working systems, own end-to-end training pipelines, and operate across transformer training, distributed systems, and evaluation. The role sits between research and engineering — you're expected to read papers, implement architectures, debug large-scale training issues, and reason about model behavior and optimization at a deep level. What You'll Own • Implement research ideas in PyTorch or JAX and translate them into production-level systems, • Run transformer training across distributed environments, • Build and operate end-to-end pipelines covering data, training, and evaluation, • Identify and resolve divergence, instability, and throughput issues at scale, • Optimize training performance and system efficiency, • Work closely with researchers to evaluate architectures and learning dynamics, • Contribute to experimentation across model behavior and optimization strategiesRequirements, • Strong experience with PyTorch or JAX, • Hands-on experience with transformer training, distributed systems, and evaluation frameworks, • Ability to debug large-scale training issues including instability and performance bottlenecks, • Experience implementing research ideas and custom architectures, • Strong understanding of model behavior, optimization, and learning dynamicsNice to Have, • Experience with Megatron-LM, DeepSpeed, xformers, or similar frameworks, • Background at frontier AI labs or top research engineering teams, • Open source contributions to ML frameworks, • Strong mathematical foundation in optimization or learning theoryThis Role Is NOT For, • MLOps or infrastructure-focused profiles without model training experience, • Applied ML roles not involved in pretraining or model development, • Backgrounds focused on deployment rather than training systemsLogistics, • Role is fully onsite in San Francisco — please only apply if you can commit to this

Job Description About us: Let's be real, AI isn't magic; Legion was built to move beyond AI hype—delivering secure, reliable systems that work alongside the people tackling the world's most critical challenges. Born from a Department of Defense partnership and trusted by leaders across government and enterprise, Legion embeds intelligence inside complex systems, unlocking data, accelerating human workflows, and strengthening mission-critical systems. We don't replace workflows—we optimize them, ensuring quality, efficiency, and reliability inside the platforms our partners already use. With world-class collaborators like Palantir, Nvidia, HPE, and Oracle, we're building intelligent infrastructure that enhances human capability and drives impact at the edge and across a range of enterprises. We're looking for bold thinkers and doers to join us in shaping the future of AI that's secure, grounded, and built to work. Job Summary: Are you an experienced Applied Machine Learning Engineer who thrives in a fast-paced, collaborative team? We seek a highly skilled Senior Applied Machine Learning Engineer to join our Applied ML team. In this role, you will leverage your expertise in AI/ML engineering to design, develop, and deploy innovative machine learning and algorithmic solutions. If you are adept at building models that solve hard problems, we encourage you to apply. You will collaborate closely with platform engineers and product partners, bringing a strong product orientation to your ML work. Responsibilities: • Lead the development of machine learning models and data-driven algorithms for high impact projects, • Collaborate with product and platform teams to own ML solutions end-to-end, • Understand the runtime complexity of algorithms and the cost to run ML models at a production scale, • Clearly communicate modeling decisions, tradeoffs, and limitations to technical and non-technical stakeholders, • Build, deliver, and maintain enterprise products, ensuring they meet high-quality standards while shipping fast, • Take ownership of your work, from design to implementation and maintenance, and drive projects to successful completion, • Support production systems, handling debugging challenges in distributed systems to ensure reliability and uptime, • Bachelor's or Master's degree in Computer Science, Machine Learning, NLP, • Minimum 5+ years of experience in applied machine learning, with a track record of delivering enterprise products, • Proven experience in Generative AI, including fine-tuning, optimizing, and evaluating LLMs, RAG pipelines, and Agentic AI systems, • Strong proficiency with AI/ML/NLP techniques, resources and methodologies (ex: Huggingface, Spacy, Scikit-learn, Pytorch)., • Strong communication skills and the ability to collaborate effectively with cross-functional teams, • Comfortable supporting production systems and debugging challenges in distributed systems, • Demonstrated effectiveness in using AI tooling to accelerate research and development workflows, • Start-up experience or comfort in 0→1 product environments., • Familiarity with Kubernetes is a plus., • Experience integrating machine learning models and data driven algorithms into larger system architectures.Compensation Information$205,000—$260,000 USD

Job Description About Flexion: At Flexion, we’re building the intelligence layer powering the next generation of humanoid robots. Our mission is to accelerate the transition from fragile prototypes to real-world humanoid deployment. We are founded by leading scientists in robot reinforcement learning (ex-Nvidia, ex-ETH Zürich), and backed by leading international VC firms. In just months, we’ve gone from our first line of code to deploying real humanoid capabilities. Fifty of the world's best robotics researchers are already building the future at Flexion's headquarters in Zurich. Among them are Nikita Rudin, David Hoeller, Julian Nubert, and Korrawe Karunratanakul - scientists who have redefined what humanoid robots can do. Now we are building their counterpart team in the United States, in San Francisco. The Role: If you are among the most capable, and ambitious robotics researchers in the country, someone who has stared at the limits of what robots can do today and decided that simply wasn't good enough, we want to hear from you. You will join the first wave of US research scientists at Flexion, set the agenda together, own the problems that matter most, and deploy real solutions on real hardware. You will work directly with a world class team across two continents on challenges that have no published answers yet. Key Responsibilities: • Drive original research in learning-based robotics - from concept to hardware deployment, • Own your research direction: define problems, design experiments, and iterate fast, • Collaborate directly with Flexion’s Zurich research team across Manipulation, Motion Generation, and Perception, • PhD in Robotics, Machine Learning, or a closely related field - completed or near completion, • Proven track record of deploying learning-based controllers on real robotic hardware, • Expert-level knowledge in:, • Reinforcement learning, • Physics-based simulation (Isaac Gym/Lab, MuJoCo, or equivalent), • Python and PyTorch, including training large-scale neural networks, • Deep expertise in at least two areas of the following:, • Diffusion models, • Flow matching, • Dexterous manipulation, • Sim-to-real transfer and real-to-sim calibration, • Whole-body control and loco-manipulation, • Synthetic data generation for robot learning, • Vision Language Model Fine-Tuning (SFT and RL-based), • Competitive compensation package, • Comprehensive health and retirement plan benefits, • A front-row seat at one of the world’s most ambitious robotics companies, • An energetic, collaborative team with a relentless bias for action, • The opportunity to build something no one has ever done in this field - alongside the world’s leading researchers

Job Description Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster. We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI. We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services. If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe. Crusoe’s Data Center Infrastructure Engineering (DCIE) team is fundamental to our mission of providing AI hardware and infrastructure as a service. The team provides infrastructure for Crusoe’s fleet GPU’s and data center. The team sits at the nexus of high performance computing and AI infrastructure as a service. The DCIE team owns, deployment maintenance, observability, critical environment, and automation. The team builds, and maintains GPU clusters, develops automation for logical and physical maintenance, provision systems, and observability tooling. About the Role: We are seeking a highly skilled and motivated Software Engineer to join Crusoe’s Data Center Infrastructure Engineering team. This position is focused on the development of software for the management of a fleet of GPU servers as well as the data centers that house those systems. The role focuses on the developing and implementing advanced diagnostic, observability, automation and repair tooling for high-performance GPU compute clusters. The ideal new team member will be a hands-on problem solver who is comfortable working independently. The new team member will play a critical role in maintaining the health and scalability of Crusoe’s rapidly growing GPU fleet. What You’ll Be Doing: * Developing and implementing deep-level diagnostics and troubleshooting of hardware faults within GPU racks and high-density compute systems. * Developing troubleshooting and automation tooling for GPU platforms including NVIDIA A100, H200, GB200, B200 and AMD 350X / 355X. * Developing automation and AI agents for executing component-level diagnosis and remediation for failed or degraded hardware. * In conjunction with data center operations develop innovative tooling and AI agents for managing the critical environment. * Developing tooling for post-repair validation and testing tools such as burn-in, Pytorch, and NVIDIA NCCL to ensure system stability and performance. * Own the deployment, monitoring, and operational support of developed tooling, ensuring solutions maximize GPU fleet availability and performance to drive customer success. * Developing automation and operational tooling for facilities management power as well as direct liquid cooling hardware systems What You’ll Bring to the Team: * Software engineering experience. * The ability to identify a problem, rapidly develop a scalable solution and ship it. * Ability to lean in and assist team members working on critical or complex technical initiatives. * Ability to set the technical direction for a specific project and execute. * Expertise in distributed systems, reliability, and cloud platforms (Kubernetes, IaC, GCP etc.) * Strength in at least one programming language - Go, Python, Java, Rust. * Strong analytical and problem-solving skills. * Excellent communication and collaboration skills. * Ability to work independently and within a team Nice to Have: * Experience with Temporal and Kubernetes. * Experience working directly with hardware vendors. * Background in large-scale GPU fleet operations or hyperscale data center environments. Benefits: * Industry competitive pay * Restricted Stock Units in a fast growing, well-funded technology company * Health insurance package options that include HDHP and PPO, vision, and dental for you and your dependents * Employer contributions to HSA accounts * Paid Parental Leave * Paid life insurance, short-term and long-term disability * Teladoc * 401(k) with a 100% match up to 4% of salary * Generous paid time off and holiday schedule * Cell phone reimbursement * Tuition reimbursement * Subscription to the Calm app * MetLife Legal * Company paid commuter benefit; $300 per month Compensation Range Compensation will be paid in the range of up to $208,000 - $253,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicants knowledge, education, and abilities, as well as internal equity and alignment with market data. Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.

Job DescriptionAbout Origin Origin is building Physical AI for the built world - starting with autonomous robots for Interior Construction. We are building Construction Action Models which allows our modular robots to learn, adapt and work in unstructured construction job site. Our robots are already deployed on live sites in New York City, helping accelerate schedules for large-scale commercial projects while improving safety and predictability on the job site. Backed by Tier-1 investors, Origin is working to close the gap between America’s surging demand for housing, data centers, and manufacturing infrastructure, and the construction industry’s growing labor shortage. The Role Our system runs a Multi Agent Action Expert architecture: classical precision algorithms orchestrated alongside learned policies. The job is systematically expanding the learned components while keeping the system production-safe. You own the full lifecycle of learned components on OG-1: from data collection and model training through edge deployment on Jetson AGX Orin. Every research project will have a deployment milestone. This is not a lab position. What You Will Do • Train and deploy VLA models for contact-rich manipulation using our imitation learning infrastructure., • Build the data flywheel: teleoperation pipelines (GELLO, SpaceMouse, VR), DAgger-style online correction, demonstration curation., • Research and prototype world models for surface state prediction, spray dynamics, and anomaly detection., • Design offline evaluation metrics that predict real-world finishing quality before deployment., • Optimize models for edge: TensorRT compilation, latency profiling, memory budgeting on dual Jetson AGX Orin., • BS/MS/PhD in CS, Robotics, ML, or equivalent experience shipping learned systems on physical robots., • Strong Python and PyTorch; comfort modifying research codebases (you'll work directly with open-source VLA implementations)., • Experience in at least two of: imitation learning, RL, vision-language models, robot learning from demonstration, sim-to-real., • Track record deploying ML on real hardware: not just training to convergence, but debugging why the policy fails on the actual robot., • Working knowledge of ROS2 or equivalent robotics middleware., • Experience working with Simulation Systems like Isaac Sim., • GPU profiling and optimization (TensorRT, ONNX, CUDA); you understand why 200ms policy latency kills contact control. Strong Plus • Hands-on with VLA architectures (π0/π0.5, OpenVLA, RT-2, Octo) or foundation model fine-tuning for robotics., • Teleoperation data collection and DAgger/HG-DAgger pipelines., • World model architectures (DreamerV3, V-JEPA, latent dynamics models)., • Construction, manufacturing, or contact-rich industrial domains., • Publications at CoRL, RSS, ICRA, NeurIPS: valued but equivalent shipped work counts.

Job Description Location: San Francisco, CA (Hybrid) What is Verse? Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world's largest energy buyers, managing energy has never been more complex or more critical. Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria, brings together energy, finance, and operations teams with real-time, finance-ready intelligence—replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs. The Role Verse is seeking a Data Scientist to join our Data Science Team. In this role, you will lead the development and deployment of advanced data-driven solutions across a range of applications, including electricity markets, renewable procurement, and energy risk management. You will shape the machine learning and data modeling foundations that Verse's software is built on. For example, you might spend a cycle deploying electricity market price forecasting pipelines for new regions, developing solar production anomaly detection models, or creating scalable tools for benchmarking energy project financial performance. Key Responsibilities • Lead End-to-End Data Science Projects: Own and drive projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions., • Statistical & Machine Learning Modeling: Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models) to support decision-making and enhance product capabilities., • Analytics Engineering & Data Modeling: Perform complex data transformations and develop well-structured data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets., • Software Development & Productionization: Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems in a cloud-based environment while leveraging AI coding tools to accelerate development., • MLOps: Contribute to Verse's machine learning modeling infrastructure to support scaling of ML models and improving reliability, monitoring, and performance in production., • Master's degree in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field and 2+ years of professional experience in data science or machine learning; or, • Bachelor's degree and 4+ years of professional experience in data science or machine learning, • Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation, • Experience deploying and maintaining models in cloud-based environments (e.g., AWS, GCP, or Azure) using MLOps practices, • Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow), • Hands-on experience in orchestrating complex data transformations (e.g. Airflow, Dagster, dbt), • Strong software engineering practices (version control, testing, code reviews, CI/CD), • Experience in energy, climate tech, or related domains (not required), • Familiarity with optimization methods or operations research, • Experience developing probabilistic forecasting models and quantifying uncertainty, • Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first, • Be Honest & Transparent: We prioritize effective communication to build trust with our team, customers, and stakeholders, • Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection $146,000 - $172,000 This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs. Benefits and Employee Perks • Competitive compensation and equity grant at a high-growth start up, • Comprehensive benefits package including medical, dental and vision insurance, and 401k, • Flexible hours and unlimited PTO

OpenAI, Dall-e, Langchain, Retrieval Augmented Generation (RAG)) and experienced with ML packages like scikit-learn and PyTorch.

Job DescriptionDraup is a Series A-funded agentic AI company building the intelligence layer for how global enterprises make workforce and go-to-market decisions. We work with 250+ enterprise clients — including 5 of the Fortune 10 — processing 1B+ job descriptions, 850M+ professional profiles, and signals from 100+ labor databases.We are now building our Silicon Valley engineering team — a small, senior group focused on next-generation AI research and product.What you'll do• Build and maintain production-grade LLM pipelines and agentic workflows.• Design and optimize RAG architectures using vector databases (Pinecone, FAISS, Weaviate) at scale.• Implement agentic systems using LangGraph, LlamaIndex, or equivalent: tool use, multi-agent coordination, and reasoning loops.• Own prompt engineering, model versioning, evaluation (RAGAS, DeepEval), and LLMOps instrumentation.• Integrate AI features into large-scale data pipelines; maintain observability and guardrails in production.What we require• BS/MS in Computer Science, Machine Learning, or related field.• 3–5 years of AI/ML engineering; minimum 2 years building LLM-powered systems shipped to production.• Strong Python; PyTorch or Hugging Face Transformers; AWS or GCP; Docker/Kubernetes.• Portfolio of shipped AI work required — agentic pipelines, RAG systems, or fine-tuned models.• No visa sponsorship. Must be authorized to work in the US without current or future employer sponsorship. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Job Description Location: San Francisco, CA (Hybrid) What is Verse? Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world's largest energy buyers, managing energy has never been more complex or more critical. Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria, brings together energy, finance, and operations teams with real-time, finance-ready intelligence—replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs. The Role Verse is seeking a Senior Data Scientist to join our Data Science Team. In this role, you will lead the development and deployment of advanced data-driven solutions across a range of applications, including electricity markets, renewable procurement, and Behind-The-Meter (BTM) battery storage. You will play a critical role in shaping our modeling and analytical data layer, leveraging machine learning techniques, supporting optimization modeling infrastructure, and developing scalable approaches to power Verse's software offerings. This position emphasizes strong technical depth in Python, machine learning, and analytical modeling, along with the ability to independently scope and execute complex projects end-to-end. Experience in electricity markets and energy systems is preferred. Key Responsibilities • Lead End-to-End Data Science Projects: Own and drive large projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions with minimal oversight., • Statistical & Machine Learning Modeling: Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models, optimization-linked models) to support decision-making and enhance product capabilities., • Analytics Engineering & Data Modeling: Perform complex data transformations and develop well-structured analytical data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets, with an emphasis on dimensional modeling and reproducibility (e.g., dbt-style workflows)., • Software Development & Productionization: Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems and model deployment pipelines in a cloud-based environment., • Exploratory Data Analysis & Insight Generation: Apply statistical methods and data exploration techniques to uncover insights, validate assumptions, and inform modeling approaches., • Machine Learning and MLOps: Contribute to Verse's machine learning modeling infrastructure to support scaling of ML models and improving reliability, monitoring, and performance in production., • Cross-Functional Collaboration: Partner with product, engineering, and business stakeholders to ensure models and insights are aligned with user needs and effectively integrated into workflows., • Master's degree or higher in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field. A bachelor's degree with significant relevant experience may be considered., • 5+ years of professional experience in data science, machine learning, or a related field, • Proven track record of independently leading and delivering complex modeling or data science projects, • Experience deploying and maintaining models in production environments, • Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow), • Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation, • Experience in energy, climate tech, or related domains (not required), • Familiarity with optimization methods or operations research, • Experience with real-time or streaming data systems, • Prior experience mentoring or leading technical teams, • Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first, • Be Honest & Transparent: We prioritize effective communication to build trust with our team, customers, and stakeholders, • Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection $190,000 - $230,000 This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs. Benefits and Employee Perks • Competitive compensation and equity grant at a high-growth start up, • Comprehensive benefits package including medical, dental and vision insurance, and 401k, • Flexible hours and unlimited PTO

Job DescriptionZyphra is an artificial intelligence company based in San Francisco, California. The Role: As a Research Engineer - Agency and Reasoning, you will be a core contributor to Zyphra’s Agency and Reasoning Team. You will be involved with performing novel research in reinforcement learning, post-training, and human preference learning, and applying your ideas at scale to our next generation of language models. What We’re Looking For / Requirements: * Strong research taste and intuition * The ability to work through a research project from conception to execution to write-up * Strong implementation and prototyping skillset * A researcher who can take an idea from conception to experimentation extremely quickly * The ability to work well and cooperate with others in a high-paced research setting * Curiosity, interest, and joy in understanding intelligence. Qualifications / Additional Skills: * Experience and aptitude with reinforcement learning, either in the context of language model reasoning or more classical RL tasks * Experience with language-model-supervised fine-tuning and preference-learning methods, such as DPO and simPO. * Experience with context-length extension methods * A good intuitive ability to understand model behaviors and correct them through iterative fine-tuning * Interest in grappling in detail with data and spending significant time involved in data engineering and synthetic data generation * Postgraduate degree in a scientific subject (Computer Science, EE/EECS, Mathematics, Physics) * Previously published machine learning research in well-respected venues * Highly proficient with PyTorch and Python * We are excited and able to rapidly learn new fields and implement new ideas * Excellent communication and collaboration skills, and can work effectively on both research and engineering implementation at scale Why Work at Zyphra: * Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued * We strongly value new and crazy ideas and are very willing to bet big on new ideas * We move as quickly as we can; we aim to minimize the bar to impact as low as possible * We all enjoy what we do and love discussing AI Benefits and Perks: * Comprehensive medical, dental, vision, and FSA plans * Competitive compensation and 401(k) plan * Relocation and immigration support on a case-by-case basis * In-office snacks and meals provided * Unlimited PTO and company holidays * In-person team in San Francisco with a collaborative, high-energy environment

Job DescriptionDraup is a Series A-funded agentic AI company building the intelligence layer for how global enterprises make workforce and go-to-market decisions. We work with 250+ enterprise clients — including 5 of the Fortune 10 — processing 1B+ job descriptions, 850M+ professional profiles, and signals from 100+ labor databases.We are now building our Silicon Valley engineering team — a small, senior group focused on next-generation AI research and product.What you'll do• Build and maintain ML models for classification, extraction, trend detection, and predictive scoring on large structured and unstructured datasets.• Design experiments and benchmarks to measure model accuracy, reduce bias, and validate outputs at scale.• Apply NLP techniques — embeddings, NER, text classification — to real-world data pipelines.• Partner with engineering to move models from experimentation to production; own monitoring and drift detection.• Build evaluation frameworks for AI-generated outputs across multiple product use cases.What we require• BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field.• 3–5 years of applied data science; minimum 2 years working with NLP or large-scale text data in production.• Strong Python (pandas, scikit-learn, PyTorch or TensorFlow); proficient in SQL.• Demonstrated track record of shipping models into production, not just producing analysis.• Experience with embedding models and semantic similarity at enterprise scale.• No visa sponsorship. Must be authorized to work in the US without current or future employer sponsorship. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Job DescriptionZyphra is an artificial intelligence company based in San Francisco, California. The Role: As a Data Engineer - Multimodal Systems, you will be a core contributor to creating, collecting, and improving Zyphra’s datasets and data pipelines across a variety of modalities. Your work will intersect with almost every team at Zyphra. You will be involved in collecting large-scale datasets and implementing and optimizing highly parallel data pipelines. You’ll Work Across: * Large-scale data collection across a variety of modalities (text, audio, image) * Designing and working with highly efficient, parallelized data processing pipelines across modalities * Designing and running rigorous experimental ablations to demonstrate the impact of new data improvements What We're Looking For / Requirements: * Strong implementation and prototyping ability * Can take an idea from conception to experimentation quickly * The ability to work well with others in a high-paced research setting * Can rapidly learn new fields and are excited to implement new ideas * Excellent communication and collaboration skills, and can work effectively on both research and engineering implementation at scale. Qualifications / Additional Skills: * Experience collecting, handling, and processing large datasets * Experience with parallel Python programming frameworks such as Dask * Understanding of the state-of-the-art in dataset curation across modalities * A generally meticulous nature and a strong interest in actually looking at data and sanity checking things * Strong grasp of proper experimental methodology for running rigorous ablations and other hypothesis testing * Understanding of and interest in large-scale, highly parallel data processing pipelines. * Proficiency with PyTorch and Python. * Experience contributing to large pre-existing codebases and rapidly getting up to speed. * Previously published machine learning research in well-respected venues. * Postgraduate degree in a scientific subject (Computer Science, EE/EECS, Mathematics, Physics, Machine Learning) Why Work at Zyphra: * Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued * We strongly value new and crazy ideas and are very willing to bet big on new ideas * We move as quickly as we can; we aim to minimize the bar to impact as low as possible * We all enjoy what we do and love discussing AI Benefits and Perks: * Comprehensive medical, dental, vision, and FSA plans * Competitive compensation and 401(k) plan * Relocation and immigration support on a case-by-case basis * In-office snacks and meals provided * Unlimited PTO and company holidays * In-person team in San Francisco with a collaborative, high-energy environment

Job DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI — and a true passion for experimentation and creation — to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own • Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A)., • Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows)., • Develop rigorous evaluation and safety frameworks — automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production., • Drive LLM and ML model development — train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges., • Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership., • Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI.What We're Looking For, • 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains., • Experience working on analytic engines (or advanced analytics platforms) — designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale., • Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms., • Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix)., • Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors., • A builder/experimenter mindset — you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems., • Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats.Bonus Points, • Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility)., • Multi-cloud experience (AWS, Azure, GCP)Why Medeloop, • Ownership from day one: small team, high-trust, no layers between your work and its impact, • Technically ambitious: you'll build AI-powered workflows, not just support them, • Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter, • Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job Description Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster. We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI. We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services. If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe. About the Role: Crusoe's Cloud Product team is hiring a Senior Staff Technical Program Manager — a recognized technical and programmatic authority who owns outcomes across a portfolio of Crusoe's most strategically significant programs. This role requires a player-coach: personally hands-on when it matters, and equally effective at shaping how the TPM function operates, elevating other TPMs, and driving the organization from reactive to predictable across the full program portfolio. Our vision is the easiest-to-use AI purpose-built cloud. We offer IaaS products, letting AI/ML engineers focus on AI model frameworks (PyTorch, Ray) and compute stacks (CUDA, ROCm) while Crusoe manages the underlying complexity. Our platform standardizes firmware/OS bundles and automates component orchestration for consistent, scalable infrastructure. We also offer AI Managed Services, like SLA-bounded Managed Inference. The TPM connects engineering, product, procurement, and data center operations to deliver a reliable platform where customers run AI workloads without managing low-level system details. At this level, TPM engagement spans the full program portfolio: owning the introduction of new GPU generations, managing a series of IaaS products, and driving the highest-stakes company-level cross-functional programs. The Senior Staff TPM shapes how programs get executed at Crusoe — not just executing within existing frameworks but defining and evolving those frameworks as the company scales. The ideal candidate is a deeply technical, strategically influential leader who has operated at VP/SVP level at a hyperscaler or neocloud, shaped how engineering organizations deliver infrastructure programs, and brings a portfolio-level perspective to every decision. What You'll Be Working On: * Portfolio-Level Program Ownership: Own a series of IaaS products or a defined section of the product portfolio — managing sequencing, resource allocation, and cross-product dependencies across multiple simultaneous releases. Ensure coherence across the portfolio, not just delivery of individual programs. * GPU Generation Ownership: Own the introduction of a new GPU generation — spanning multiple SKUs, architectural transitions affecting networking, storage, and cloud foundations, and the operational and commercial readiness required to serve customers reliably at scale. Manage compounding dependencies between new-generation NPI work and active production deployment programs running in parallel. * Highly Cross-Functional Program Leadership: Drive company-level cross-functional programs — proactively identifying the highest-risk areas before they become visible to leadership, helping teams design mitigation plans ahead of issues, and maintaining forward-looking situational awareness across organizational boundaries. * NPI Framework Evolution: Define and evolve the commissioning gate framework and NPI/NTI engagement model as Crusoe's deployment scale and product complexity grow. Anticipate specialist pool conflicts between NPI programs and active production deployments. * Capacity & Investment Planning: Own portfolio-level capacity planning — TPM bandwidth allocation, specialist pool management, escalation paths, and operating cadences (MBRs, QBRs, OKRs). Ensure high-priority programs are adequately resourced before they become critical path risks. * Coaching & Elevation: Mentor and develop Staff and Senior TPMs. Raise the technical depth and execution standard across the TPM function. Define what good looks like and build toward it systematically. * AI-Native Program Leadership: Set the standard for AI tool integration across the TPM team — identifying where AI materially changes program tracking, risk detection, and executive communication, and driving adoption across the organization. What You'll Bring to the Team: * Portfolio-Level Technical Fluency: Current, accurate mental model of the AI IaaS and Managed Services stack across all active programs — GPU SKU generations, firmware lifecycle states, SDN and storage architecture, cluster topology, and the commissioning maturity of each active deployment. Understands how technical decisions at the infrastructure layer cascade into customer SLA commitments and revenue impact. * GPU Generation Expertise: Has personally owned the introduction of a new GPU generation or equivalent large-scale platform transition at a hyperscaler or neocloud — spanning vendor relationships, architectural updates, cloud foundations work, and first customer delivery at scale. * Experience at Hyperscalers or Neoclouds: 12–18 years of experience as a Technical Program Manager, with significant tenure in senior IC roles at hyperscalers (AWS, Azure, Google, Meta, OCI) or neoclouds (CoreWeave, Lambda Labs). Must have operated at the VP/SVP stakeholder level and shaped how engineering organizations execute infrastructure programs — not just driven individual programs. * NPI Framework Design: Has defined or significantly evolved NPI engagement models, commissioning gate frameworks, or program execution standards adopted across a large engineering organization. Brings opinions about what's broken in how hyperscalers run GPU NPI and what a better model looks like. * AI Tool Leadership: Defines how AI fundamentally changes the TPM craft — not just personal tooling adoption but how AI reshapes risk detection, program tracking, executive communication, and the skills required across the TPM team. * Strategic Influence: Proven ability to shape VP/SVP-level investment decisions, frame infrastructure tradeoffs in business terms, and drive alignment across organizational boundaries without formal authority. * Executive Communication: Exceptional ability to synthesize complex, multi-program technical realities into crisp, decision-ready executive narratives. Delivers updates that surface the right decision elements without requiring follow-up. Bonus Points * Bachelor's or Master's degree in Engineering, Computer Science, or a related technical field * Engineering background — hardware engineering, systems engineering, silicon validation, or firmware — that provides genuine below-the-OS technical credibility * Experience introducing a new chip vendor or GPU generation at fleet scale * Experience building TPM organizations or functions from early stage at a hyperscaler or hyper-growth company What This Role Is Not * Not a PMO or process architecture role: You are hands-on when it matters and your credibility comes from personal execution, not from designing operating models for others. * Not a People Management Role: You will not have direct reports, though you will coach and elevate other TPMs across the organization. * Not a role for someone who needs structure handed to them: You define structure at the portfolio level and create clarity for others to execute on programs without internal precedent. Benefits: * Competitive compensation and equity packages * Restricted Stock Units * Paid time off, paid holidays & leave of absence programs * Comprehensive health, dental & vision insurance * Employer contributions to HSA account * Paid parental leave * Paid life insurance, short-term and long-term disability * Professional development & tuition reimbursement * Mental health & wellness support * Commuter benefits (parking & transit) * Cell phone stipend * 401(k) Retirement plan with company match up to 4% of salary * Volunteer time off * Global travel insurance & emergency assistance * Daily meals allowance * Additional perks & programs specific to location Compensation Range Compensation will be paid in the range of up to $216,150 - $262,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicants knowledge, education, and abilities, as well as internal equity and alignment with market data. Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.

Job DescriptionTHE OPPORTUNITY We are Amplifier, and we have built the world’s first Large Acoustic Model (LAM), a foundation model that uses human voice to detect health conditions. This is sci-fi becoming reality. We have raised significant capital from top-tier investors to turn this technology into a massive new category in healthcare. We are looking for a researcher who is tired of the "publish or perish" cycle and wants to build intelligence that actually works in the real world. THE REALITY Let’s be clear about what we are signing up for. We are entering a phase of hyper-growth. We are pushing ourselves—and this technology—further than most would consider reasonable. We are doing this because we believe the outcome (saving lives at scale) is worth the intensity required to get there. • We work in person in San Francisco. We believe that the hardest problems are solved at a whiteboard, not over a Zoom call. We want the energy, the speed, and the camaraderie that comes from being in the arena together., • We move fast. We don't spend months on theoretical proofs. We hypothesize, we code, we train, and we validate. The feedback loop is immediate. The Challenge: • Novel Architectures: Voice is not text. You will push the boundaries of how Transformer architectures process long-range acoustic dependencies and non-verbal signals., • Biomarker Discovery: You will design experiments to isolate specific acoustic features (jitter, shimmer, respiratory rate) that correlate with health conditions, often discovering signals that medical science hasn't yet quantified., • A Builder-Researcher: You have a deep theoretical understanding of Deep Learning (maybe a PhD, maybe not—we care about ability, not pedigree), but you express your ideas in PyTorch, not just LaTeX., • Audio Native: You understand the physics of sound. You know your way around DSP (Digital Signal Processing), STFTs, Mel-spectrograms, and the unique challenges of modeling raw audio., • First Principles Thinker: You don't just import HuggingFace libraries. You understand the math behind the attention mechanism and can modify it when standard approaches fail., • Impact: The chance to build a product that literally saves lives., • Equity: Real ownership. We are early enough that your equity package has life-changing potential., • The Team: You will work directly with the Founders (Jeremy, Amit, Peh) and our AI research team. No middle management. Just builders. Send us your GitHub or a link to a paper/project you implemented. Tell us about a time you had to abandon a standard architectural approach because it didn't work for your specific data problem. Come build with us.

Job Description The Research Role: We're looking for an experienced AI Researcher to join our team and help us push the boundaries of AI and anime. You'll design and develop large-scale diffusion models with a primary focus on images, and secondary focus on other modalities like 3d, text, video and audio. You may be a good fit if: You love anime and the anime aesthetic. This one of the only labs in the world where you can combine your expertise on anime and deep learning to work on state-of-the-art anime models. You understand diffusion, or can get up to speed quickly. We're looking for experienced researchers who aren't afraid of jumping headfirst into experimenting and designing massive models. You're proficient in Pytorch (and maybe JAX too) We have a very large Pytorch codebase, and an equally large JAX codebase for large-scale TPU training. You enjoy a tight loop between research and production. We pride ourselves on getting models from the lab into production quickly. Our research team works closely with our internal studio to both develop game-ready models, and deploy regular quality updates to niji・journey. You're comfortable working on small, fast-paced teams We currently have a team of four on AI. You'll be working closely alongside some of the best AI researchers in the world, on the literal best image model in the world. We also believe in the unmatched speed of in-person teams, and prefer on-site collaboration in either our primary research office in Tokyo (downtown Akihabara), or San Francisco. Visa sponsorships are available.