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

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Build and own production-grade machine learning systems spanning data, training, evaluation, deployment, and observability. Translate research prototypes into reliable infrastructure, establish engineering standards, maintain experimentation pipelines, and partner closely with researchers. The role requires strong distributed systems experience, Python expertise, familiarity with PyTorch or JAX, and experience with tools such as Ray, Slurm, or Kubernetes.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Lead the design, training, evaluation, and deployment of large-scale machine learning models, including LLMs, diffusion models, and GNNs. Own distributed training pipelines, dataset interfaces, optimization strategies, model evaluation, serving, and lifecycle management. Collaborate with data, infrastructure, and scientific teams while providing technical leadership through architecture reviews, mentorship, and cross-functional decision-making.

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Design, build, and deploy large-scale machine learning models powering Snapchat features. Own full ML lifecycle from data analysis to production, prototype with cross-functional partners, apply modern ML techniques, and use AI tools while ensuring code correctness, security, and scalable system design.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Build and train transformer models for plant intelligence, owning data curation, architecture, training strategies, evaluation, and production deployment. Develop robust model lifecycle systems and training and evaluation infrastructure for rapid iteration. Drive machine learning projects end to end, from data collection through production, while working independently on a small team. Apply strong software engineering and scientific skills to maintain practical ML systems.

Lead PayJoy’s ML platform organization and manage Staff and Senior ML and software engineers. Define the long-term roadmap for ML infrastructure, feature stores, model serving, ETL, internal APIs, deployment, monitoring, and MLOps. Partner with Risk, Fraud, Engineering, Product, and Data Science stakeholders to translate business goals into scalable technical solutions. Mentor engineering leaders, ensure production reliability and compliance, identify operational bottlenecks, and guide adoption of technologies such as LLMs and graph databases.

The Senior Machine Learning Engineer will design innovative machine learning models for drug discovery, leveraging large-scale single-cell datasets and collaborating with ML Scientists to develop optimized models.

Develop and improve machine learning models for Pinterest's various product surfaces, utilizing deep learning and advanced data processing.

Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.

Lead and grow a team building large-scale reinforcement learning and ML behavior models for autonomous driving. Set technical strategy, coordinate with partner teams, oversee training and efficient inference, monitor KPIs, and manage team resources.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

Design, implement, and deploy production-scale machine learning models to evaluate disputes, chargebacks, and fraud likelihood. Build and manage orchestration layers for multiple models, collaborate with stakeholders to integrate partner/customer data, and improve automated and supervised decision-making for a product-focused ML environment.

As a Machine Learning Engineer, you'll own the design, development, and operation of production-grade ML systems in clinical workflows, ensuring model accuracy, reliability, and integration into patient-facing applications.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

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.

The Senior ML Software Engineer will create and improve capabilities for mapping data using machine learning, collaborating with engineers and data scientists, developing models, and writing production code.

Build and operate scalable ML inference infrastructure powering Claude’s safety systems. Responsibilities include productionizing safety research, optimizing real-time inference latency and throughput, developing monitoring and observability tools, implementing testing and deployment automation, and supporting reliable classifier rollouts. The role requires collaboration with researchers, inference engineers, security teams, and alignment teams to deliver high-throughput, low-latency, safety-critical ML systems.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.