Top Machine Learning Jobs in San Francisco Bay Area, CA

As a Data Scientist, you will develop algorithms, collaborate on product requirements, and communicate data-driven insights, while enhancing product offerings and delivering actionable insights.

Develop, test, and deploy production-scale machine learning and statistical models for tennis sports betting. Create contextualized feature sets, run offline and online experiments to improve performance, collaborate with engineering and product teams, follow software engineering best practices, document work, and present results to technical and non-technical stakeholders.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

Lead development of data pipelines powering ML for motion planning and decision making. Prototype pipeline and model improvements, fine-tune and evaluate models, deploy and validate models on fleet with monitoring, collaborate with perception/research/simulation teams, and mentor engineers in data-driven reasoning approaches.

The Research Engineer will focus on multimodal sensing systems and sensor fusion for embodied AI and robotics, evaluating sensor modalities and collaborating with hardware and ML teams to enhance robotic learning and performance.

As a Machine Learning Engineer, you will manage multimodal perception, work on data collection, and evaluate robotics tasks, directly impacting model capabilities and the evolution of physical labor.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Conduct empirical economic research on AI’s real-world impacts using large-scale datasets. Design and execute causal inference or structural modeling studies, collaborate cross-functionally, produce policy-relevant outputs (papers, reports, briefings), and develop new measurement approaches for AI-driven economic change.

Lead product marketing for Guidewire's Data Platform: own positioning and GTM, create technical sales enablement and content, drive adoption and pipeline for data, analytics, ML and AI, partner with product, engineering, and field, and present at conferences and customer workshops.

Lead design and productionization of decisioning systems that impact pricing, offers, routing, and economics. Build models, heuristics, optimization logic, and evaluation frameworks. Design and run production experiments, translate business objectives into measurable decision systems, create feedback loops linking model and product outcomes, and partner with engineers to ship model-backed logic and configurable rules. Use AI to accelerate work and balance rigor with rapid iteration.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

The role involves advising clients on AI strategy, designing AI solutions, and leading change management efforts. This includes governance, training, and client engagement while mentoring junior team members.

The Applied AI Scientist will design and implement ML models to decode brain signals, contribute to data platforms, and ensure interpretable results for medical applications.

As a Computational Protein Design Scientist, you will leverage generative AI to design proteins, improve models, and collaborate with lab teams for experimental validation.

Design new information architectures for language models, refine their interaction with data, and develop evaluation sets to improve functionality.

Lead the development of Zipline's data platform for autonomy, ensuring reliable operation and effective data logging, processing, and ML training.

The Research Engineer/Scientist will advance AI through innovative RL methods, focusing on training general-purpose agents and enhancing existing ML systems.

Research and develop improvements to machine learning models, collaborate with research and product teams, and evaluate model performance.

Develop deep learning models for autonomous vehicle trajectory generation, collaborate with cross-functional teams, and analyze system performance metrics.

Lead the Perception team in developing occupancy models and advanced AI techniques while collaborating cross-functionally. Oversee the team’s roadmap and technical strategy, ensuring high performance and reliability in complex environments.

The Machine Learning Engineer will design, develop, and implement advanced recommendation systems, collaborating with cross-functional teams to enhance personalization for a global audience.

Build core product features across Python backend and TypeScript frontend, develop a WebGPU-based rendering engine and canvas, architect AI agents for design generation and editing, and maintain evaluation and RL pipelines. Work across product, graphics, and AI, owning end-to-end technical problems and shipping high-quality user-facing product features.

Lead rapid prototyping of PoC research in computer graphics, computer vision, and machine learning to evaluate algorithms for production. Conduct R&D in human modeling and simulation, camera and image‑sensor simulation, and face/pose tracking for VR/AR hardware. Develop data-generation pipelines using graphics APIs and 3D tools, and implement ML systems with TensorFlow/PyTorch to support immersive AR/VR experiences.

Drive RL research end-to-end for coding agents: form hypotheses, design experiments, build training/eval/data pipelines, train graders for non-verifiable rewards, improve datapoint quality, and develop realtime RL solutions to scale model performance.

Design, prototype, and productionize AI systems and infrastructure—recommendations, natural language interfaces, agentic workflows—partnering with product and engineering to scale ML across the full lifecycle (data curation, training, deployment, monitoring).