Top Machine Learning Jobs in San Francisco Bay Area, CA

Research, implement, and optimize machine learning methods for high-fidelity 3D simulation and autonomous vehicle development. Responsibilities include training generative and world models, building scalable cloud pipelines, improving rendering and data-generation tooling, and integrating ML techniques into production simulation systems. The role collaborates with researchers, technical artists, and simulation engineers and focuses on 3D vision, reconstruction, radiance fields, and rare-scenario generation.

Develops and optimizes machine learning models for realistic 3D simulation, including 4D generative and world models for rare autonomous-driving scenarios. Responsibilities include researching 3D vision methods, building scalable cloud pipelines, integrating ML techniques into production simulation, and improving rendering and data-generation tools. The role requires experience with Python or C++, radiance fields, 3D or video generative models, 3D reconstruction, human-centric vision, and probabilistic techniques.

Build and operate the core runtime for an AI-driven product, including model orchestration, tool integrations, state management, retries, checkpoints, evaluations, tracing, and monitoring. Develop reliable interfaces to scientific and laboratory systems, support model-training data pipelines, optimize production performance, and collaborate with researchers and domain experts. The role also includes architectural leadership, coding standards, mentoring, and public technical contributions.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.

Leads Box’s applied AI research agenda by identifying high-impact opportunities, defining research priorities, partnering with engineering and AI labs on experiments, evaluating models and agents, translating findings into product improvements, and communicating evidence-based insights to customers, executives, media, and external audiences.

Leads high-impact data science initiatives across marketing, growth, and product. Designs experiments, causal measurement frameworks, incrementality models, uplift and heterogeneous treatment effect models, and personalization and optimization solutions. Partners with cross-functional leaders to improve acquisition, retention, ROI, conversion, and engagement. Defines technical strategy, promotes scalable workflows and AI-assisted development, communicates recommendations to executives, and mentors data scientists and analysts.

Develops agentic systems and machine learning models using frontier, genomics, and multiomics models to advance disease understanding, drug design, therapeutic response, and longevity research. The role involves building and shipping high-performing AI/ML products, applying engineering best practices, evaluating models, and collaborating across teams. Genomics and RNA biology experience is helpful but not required.

Lead the development of manufacturing automation methods for processes including die casting, injection molding, and CNC machining. Design and iterate experiments, integrate robots, manufacturing equipment, and CT scanners, and develop autonomous manufacturing processes. Work with customers to define pilot programs and travel internationally to manufacturing sites. The role requires applied machine learning, optimization, scientific computing, simulation, prototyping, and strong experimental judgment.

Leads advanced analytics and machine learning initiatives supporting clinical development and risk-based quality management. Designs statistical models, algorithms, prototypes, KPIs, reports, and visual analytics solutions using R, Python, SQL, and visualization platforms. Partners with cross-functional teams to identify clinical trial risks, generate insights, ensure regulatory and GCP compliance, and establish analytical standards. Coaches junior team members and communicates technical findings to business stakeholders.

Lead the Hardware Intelligence research agenda, developing AI agents that reason over hardware data, operate engineering tools, connect simulations with physical tests, and coordinate across systems. Build RL environments, verifiers, and post-training methods; deploy models on-device, on-premises, and in air-gapped environments; establish benchmarks; and hire and mentor researchers. The role requires setting technical direction, conducting statistically rigorous experiments, and delivering research that improves shipped products.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; build centralized experimentation frameworks; extract and analyze data with SQL and Python; mentor data scientists and communicate results to stakeholders and senior leadership.

Develop and train machine learning models for cloud microphysics, storm evolution, and weather-seeding impacts. Build data pipelines using balloon, radar, satellite, and model data; lead preprocessing, training, evaluation, and uncertainty experiments; and translate research into production forecasting tools. Collaborate with atmospheric and hardware teams on field integration, applying spatiotemporal, physics-informed, or time-series methods and distributed computing.

Staff Data Scientist partnering with Growth, Marketing, Product, and Engineering teams to build data science solutions that drive customer acquisition, engagement, and retention. Responsibilities include predictive modeling, personalization, recommendation systems, LLM applications, incentive optimization, causal inference, marketing impact measurement, technical mentorship, and strategic leadership. The role requires deploying production machine learning systems and communicating analytical insights to senior stakeholders.

Design, train, evaluate, and deploy machine learning and language models for real-time payments risk decisions. Own data pipelines, labeling, metrics, model evaluation, latency, and cost optimization. Convert user approvals into structured rules, detect fraud and prompt injection risks, assess identity and transaction signals, and make approve, confirm, or refuse decisions. The role is an individual contributor position with potential to build and manage a team.

Improve multimodal AI agents that analyze construction documents against regulatory requirements. Responsibilities include model post-training, experiment design, automated research, evaluation analysis, light data engineering, engineering tooling, web application improvements, and developing agents for new building types. The role also involves weekly user conversations and requires independent execution, rigorous evaluation standards, interest in housing supply and land-use reform, and willingness to work approximately 55 hours weekly.

Analyze autonomous vehicle, fleet, and business data to develop metrics, evaluate model and simulation performance, identify behavioral patterns, and support data-driven decisions. Projects may include drift monitoring, automated retraining, predictive modeling, similarity search, and statistical testing. The intern will use Python and SQL, apply machine learning and analytical methods, collaborate with simulation and autonomy teams, and communicate findings to varied audiences.

Applies AI and machine learning techniques to product specifications, recommendations, and agentic workflows. Responsibilities include prototyping, evaluating, and iterating on models and AI-powered customer features; analyzing structured and unstructured data; translating customer problems into technical approaches; and documenting results. The intern collaborates with Applied AI Engineers, Product Managers, and Engineering while gaining experience shipping practical AI systems.

Designs, scales, and leads machine learning systems that detect harmful content and bad actors, support content moderation, and promote platform integrity. Provides technical direction across the ML organization, partners with cross-functional leadership, improves ML infrastructure and operational excellence, and delivers performant, scalable models using modern deep learning techniques.

Lead the data science strategy and teams developing AI-powered compliance products for KYC, AML, identity verification, watchlist screening, risk modeling, and fraud prevention. Own the full model lifecycle, establish evaluation and monitoring practices, ensure explainability and governance, and partner with Product, Engineering, Risk, Operations, Legal, customers, and executives to deliver scalable, production-ready systems.

Conduct technical AI safety and alignment research by designing control protocols, monitoring systems, evaluations, threat models, tools, and datasets for frontier AI systems. Investigate deception, sabotage, reward hacking, multi-agent risks, monitorability, and recursive self-improvement. Build ML pipelines and evaluation harnesses, run experiments, collaborate with industry, government, and academia, and publish findings and policy-oriented technical reports.

Build evaluation frameworks, datasets, sandbox environments, and metrics for production AI agents. Design experiments to compare models, tools, harnesses, and execution environments; investigate failures; and improve agent reliability, performance, and cost. Contribute to agent infrastructure, reusable knowledge systems, internal tooling, and integrations while collaborating with research, engineering, infrastructure, security, and business teams. This applied research role combines production-quality software engineering with open-ended experimentation.

Build reproducible data and AI workflows using Python, SQL, notebooks, and version control. Prepare and validate structured and unstructured data, support analytics and machine learning, prototype generative AI applications, evaluate models, and contribute to applied R&D. Communicate assumptions, limitations, and findings to technical and consulting stakeholders while developing reusable tools, documentation, and training materials in a collaborative consulting environment.

Build and launch scalable distributed systems and AI-powered products for consumer incentives. Own machine learning projects end to end, including scoping, offline evaluation, experimentation, production deployment, optimization, and maintenance. Partner with product, operations, and data science stakeholders while designing large-scale systems and algorithms that improve consumer experiences and business outcomes.

Lead the technical direction and development of Uber’s real-time marketplace matching optimization systems. Build and productionize machine learning and decision systems using reinforcement learning, deep learning, causal inference, optimization, and control methods. Formulate ambiguous problems, develop behavioral models, create simulation and experimentation frameworks, and deploy scalable online inference systems. Collaborate cross-functionally, lead 0-to-1 initiatives, improve marketplace performance, and mentor engineers.

Founding Engineer will work across AI research and engineering to develop reinforcement learning and post-training systems. Responsibilities include exploring open-ended problems, building model-learning systems, moving prototypes into production, shaping technical direction, and helping establish engineering culture. The role requires strong engineering judgment, independent problem-solving, rapid execution, and enthusiasm for intelligence and model capabilities. Experience with AI agents, machine learning, reinforcement learning, post-training, early products, research tools, or technical infrastructure is preferred.