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Job DescriptionBlueSpace.ai is revolutionizing the autonomous vehicle industry with our groundbreaking self-driving technology. Unlike conventional autonomy software, our patented 4D Predictive Perception removes reliance on data. By leveraging next-gen 4D sensors, we can precisely predict the motion of all objects, increasing accuracy, lowering latency, and setting a new standard for safety and efficiency in autonomy. Our team consists of seasoned professionals from across the autonomous vehicle (AV) ecosystem, including OEMs, world-class research institutions, and leading autonomous driving companies. With a proven track record of successful AV service launches in California, Texas, and Florida, we are well-positioned to drive the next generation of autonomy. We are seeking talented, self-motivated individuals who thrive in a fast-paced, dynamic environment and are passionate about making a significant impact on the future of mobility. If you’re driven to tackle the most complex challenges in autonomy and push the boundaries of what is possible, we want to hear from you. As the ML Infrastructure Engineer at Bluespace, your mission will be to develop the Cloud Data Infrastructure System to support and enable our Assured AI for Autonomy. You will have the opportunity to design and develop the infrastructure, which will allow our developers to expand Bluespace’s APNT capabilities.Duties and Responsibilities • Take ownership of the infrastructure in support of developing and deploying Machine Learning models for Autonomous Vehicles, • Architect and deploy cloud and on-prem ML training and evaluation infrastructure, • Own the the data management pipelines, from ingestion and storage, to model training and evaluation that span vehicle compute, cloud, and on-prem, • Change model training code to take advantage of the better data storage techniques and formats you propose, • Evaluate and implement methods, software, and hardware for model deployment onto the test and production vehicles, • Develop systems and processes to improve transition of models from research to production while balancing cost, • Participate in model design, research and set requirements to model design that ensure their successful deployment, • Own and deliver projects end-to-end, • Optional: be able to hire, manage, or at least mentor other engineers who join this project when growth is neededQualifications and Experience, • Experience in architecting and implementing data engineering solutions for a small engineering team / product (1-20 ppl), • 2+ years of software engineering experience in any of the following: ML Infrastructure, Data Engineering, Platform Engineering, Distributed Systems, • Either existing experience with ML Infrastructure as described below, or strong expertise in non-ML Data infrastructure combined with a strong desire to learn ML Infra specifics, • Production ML experience with at least one of the following - (1). Model conversion and optimization for production (ONNX, TensorRT), (2) Model deployment on specialized hardware (e.g. Jetson), or (3) Model monitoring and MLOps, • Ability to programmatically access cloud services using Python, NodeJS, or equivalent, • Knowledge of or experience with data management solutions, such as - (1) Workflow orchestration pipelines (e.g. Argo, Airflow, Kubernetes) or (2) Managed large-scale data processing systems (e.g. Spark, Dataproc, Databricks), • Experience with Terraform or a similar IAC solution, • Robotics background, C++, ROS

Job DescriptionWho We AreFounded in 2016, Rhombus is on a mission to make the world a safer place with our centralized platform that combines intelligent cameras, sensors, and AI analytics to help organizations improve safety and operations at scale. We have a solid product-market fit, customers love us, and our solution makes a profound impact and difference in the world. Rhombus was created by industry veterans and is also backed by incredible investors who believe in transforming the world of physical security with enterprise-grade technology that's accessible to any organization. Who You AreAs a Technical Growth Manager, you will play a pivotal role in shaping our growth trajectory by spearheading experimentation, leveraging data-driven insights, and architecting scalable systems. You're resourceful, thrive in ambiguity, and move fast with limited direction. You enjoy building the backend systems that power growth just as much as designing the experiments that validate them. Collaborating closely with the CEO and cross-functional teams, you will be instrumental in identifying, executing, and scaling initiatives that drive revenue growth.What You'll Do • Own the experimentation roadmap across the funnel—design tests, implement tracking, analyze results, and scale winning motions., • Build and maintain the data infrastructure needed to support fast-paced testing, including ingestion, enrichment, scoring systems., • Architect scalable workflows and automations that turn successful tests into long-term, repeatable growth engines., • Analyze experiment results using quantitative/qualitative insights, making informed decisions without full statistical significance., • Lead our paid acquisition programs (Google Search, Native, Display), including campaign structure, bidding strategies, conversion optimization., • Build, launch, and optimize A/B tests across landing pages, ad copy, targeting, and channel mix to improve cost efficiency and conversion rates., • Develop top-of-funnel marketing motions—including direct response, demand capture, and early-stage awareness tactics—to drive consistent pipeline creation., • Partner closely with Sales and Marketing to brainstorm and execute net-new lead and pipeline generation ideas., • Collaborate with Product and Operations to align messaging, audience targeting, and system integrations.What We're Looking For:, • 3-5 years of experience in growth, marketing, product, data, or a technical role with direct exposure to experimentation and analytics, • 2+ years of hands-on experience with Python and/or JavaScript, plus strong SQL skills and comfort working with complex datasets, • Deep understanding of paid search and top-of-funnel acquisition, including campaign structure, bidding strategies, landing pages, A/B testing, and creative experimentation, • Experience designing, running, and scaling multi-channel growth experiments, with an ability to interpret results and operationalize winning motions, • Self-starter with strong end-to-end ownership—from spotting opportunities to scoping solutions, executing quickly, and scaling what works, • Scrappy, experiment-driven mindset with the ability to learn fast, iterate, and make decisions using imperfect data, • Bias for action and pragmatic problem-solving, with accountability for outcomes and a drive to move quickly in fast-paced environments