Top Senior Machine Learning Jobs in San Francisco Bay Area, CA

The Applied Scientist will develop and deploy machine learning products for content understanding, creator sponsorship, advertising, and brand safety. Responsibilities include selecting and evaluating ML approaches, working with behavioral, metadata, text, and video data, prototyping models, deploying them through data pipelines and ML services, and collaborating with scientists, engineers, product teams, and business stakeholders. The role also contributes to innovation in LLMs, computer vision, inference, and model evaluation.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Owns the end-to-end improvement of machine learning models after initial training, including compute setup, dataset preparation, simulated environment design, and automated self-improving workflows. The role combines applied AI research with software engineering, requiring expertise in reinforcement learning, evaluations, interpretability, PyTorch, ML pipeline debugging, and curated dataset production.

Build and scale AI/ML solutions for healthcare revenue cycle automation. The role owns AI outcomes, identifies high-value use cases, shapes technical and product roadmaps, converts prototypes into scalable production systems, and architects infrastructure for training, testing, monitoring, security, and deployment. The engineer will apply ML, NLP, and LLM approaches to claims and workflow optimization while balancing technical tradeoffs, business value, cost, and operational efficiency.

Lead the design, development, productionization, and monitoring of machine learning models and pricing algorithms for Uber’s large-scale, real-time marketplace. Develop optimization solutions using deep learning, causal modeling, reinforcement learning, and algorithmic game theory. Set technical direction, mentor engineers, collaborate cross-functionally, and deliver scalable systems serving billions of trips and over one million predictions per second.

Designs, trains, evaluates, and operationalizes intelligent document-processing solutions using OCR, machine learning, LLMs, Python, and cloud data pipelines. Builds extraction, validation, reconciliation, exception-handling, and human-review workflows for sensitive information. Develops secure, auditable, configuration-driven systems with API integrations, monitoring, testing, CI/CD, and infrastructure as code. Partners with business experts to define document fields, quality thresholds, validation rules, and acceptance criteria.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Leads end-to-end machine learning development, including neural network design, data collection, model training, tuning, deployment, monitoring, and optimization at production scale. Builds scalable code and data pipelines, develops novel ML algorithms, improves backend systems and engineering processes, conducts metric-driven research, mentors engineers, and contributes to product strategy and roadmap planning.

Develop and productionize machine learning models at scale, including neural network design, data preparation, training, tuning, deployment, monitoring, and continuous improvement. Build scalable data pipelines and secure backend systems, conduct metric-driven research, improve engineering practices, mentor junior engineers, and collaborate cross-functionally. The role requires expertise in modern ML frameworks, distributed systems, Python or shell scripting, and a machine learning specialization such as computer vision or NLP.

Develop and maintain data integration, ETL, ML, and monitoring pipelines supporting AI/ML and generative AI tools in UCSF Health’s research infrastructure. Productionize models, implement data integrations, design monitoring metrics for safety, bias, drift, performance, and validity, and collaborate with researchers, data scientists, and technical teams. The role requires advanced Python, SQL, MLOps, CI/CD, cloud architecture, and Epic Clarity/Caboodle data-model expertise.

Own the machine learning platform supporting experiment tracking, distributed multi-node training, model evaluation, and production inference. Build reliable, reproducible, and cost-efficient systems that scale from small experiments to petabyte-scale multimodal training. Collaborate with research scientists, data and software engineers, and product teams to integrate models with downstream agents. Develop training and inference interfaces, support multimodal data, and optimize performance, observability, and infrastructure costs.

Develop and deploy physical AI systems on real robots, including learned perception, vision-language-action models, imitation and reinforcement learning, world models, autonomous behavior, and sensor-data pipelines. Prototype approaches in simulation and field environments, optimize models for latency and edge deployment, and collaborate across robotics, controls, embedded, mechanical, and product engineering teams. The role also contributes to long-term Physical AI architecture and research direction.

Develop statistical and machine learning techniques, quantitative models, and scalable automation for evaluation pipelines. Build data-processing infrastructure, improve evaluation efficiency, and solve ambiguous problems across statistics, ML, and large-scale systems. Collaborate with engineers, data scientists, product managers, and leadership while maintaining code quality in a complex codebase.

Design, train, deploy, and operate machine learning systems for real-time suspicious-activity detection. Own model development, data pipelines, serving, infrastructure, scalability, and production measurement. Collaborate with technical leadership and customers to identify threats and improve detection signals. Establish engineering standards, ensure correctness, and deliver reliable systems in a high-autonomy, fast-moving team.

Lead technical development of onboard motion-planning systems for low-speed autonomous vehicle maneuvering. Improve evaluation capabilities, investigate driving issues through log analysis, develop foundational frameworks, solve challenging long-tail driving scenarios, and mentor junior engineers. The role also includes managing a team of at least five reports and collaborating across engineering teams.

Own and lead the development, migration, and operation of recommendation systems for Fable and Everand. Responsibilities include moving Fable’s recommender to the Orion ML platform, extending ranking and retrieval capabilities, incorporating social signals, managing production quality and reliability, designing A/B tests, partnering with product managers, leading cross-team delivery, and improving engineering practices.

Build and deploy manufacturing data systems, machine-vision inspection solutions, and machine-learning models across supplier factories. Integrate MES, equipment, test, image, and genealogy data to improve traceability, yield, quality, process control, and root-cause analysis. Develop automated analytics, dashboards, alerts, and reusable tools; lead deployments through NPI and mass production; collaborate with manufacturers and vendors; and define the long-term manufacturing intelligence roadmap.

Design and develop machine learning algorithms for target ROAS, automatic bidding, and ads pacing. Explore bid landscapes, optimize advertiser value and revenue, conduct A/B experiments, and collaborate with product, sales, and engineering teams on new ad products and bidding strategies. Provide staff-level technical leadership, drive cross-team alignment, and use AI-assisted development and productivity tools to improve engineering workflows.

Build and maintain scalable machine learning models and end-to-end ML pipelines for logistics automation. Responsibilities include data exploration, model deployment and monitoring, production infrastructure improvements, collaboration with engineering and product teams, code reviews, architectural guidance, and technical mentorship.

Serves as chief architect for Sony Interactive Entertainment’s Data & Intelligence organization. Defines enterprise data and machine learning architecture, standards, reusable platforms, governance, experimentation, activation, and AI lifecycle practices. Chairs architecture reviews, leads complex cross-organizational initiatives, advises executives, removes technical blockers, and mentors senior technical professionals. The role is hands-on, requiring architecture authorship, prototyping, code and design reviews, and delivery of scalable data, ML, and decisioning systems serving millions of consumers.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Develop and productize deep learning perception systems for autonomous driving, including traffic signal, sign, road marking, and construction object understanding. Lead end-to-end model development, data-driven improvement, simulation, augmentation, and deployment to embedded platforms. Optimize solutions for accuracy, safety, latency, robustness, and regional generalization while collaborating with data-labeling teams and contributing technical leadership.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.