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

Design and validate threat models for agentic AI systems, create adversarial experiments and synthetic environments, develop static and dynamic risk analysis, fine-tune and evaluate models, and build production-ready guardrails, monitoring, dashboards, and evaluation infrastructure. Optimize security systems for latency, cost, reliability, and measurable real-world performance while translating research into product capabilities.

Develop, validate, and deploy credit underwriting machine learning models. Lead proofs-of-concept for alternative data, collaborate cross-functionally on model deployment, ensure regulatory and fair-lending compliance, and present model insights to credit and business leaders.

Leads the design, development, and productionization of machine learning systems at scale. Builds and validates models, data pipelines, cloud-based ML architectures, and application code; automates testing and deployment; monitors and retrains production models; and ensures high availability, performance, security, governance, and responsible AI practices. Collaborates with Product, Data Science, and Agile engineering teams to deliver optimized ML applications.

Build and evaluate machine learning models that generate high-definition maps from raw sensor data. Develop statistical evaluation methods, scalable distributed data-processing pipelines, and backend infrastructure for global mapping. Collaborate with onboard engineering teams to improve mapping formats, manage the full modeling lifecycle, and measure system-wide impact. The role emphasizes autonomous vehicle mapping, large-scale systems, quantitative experimentation, and end-to-end software engineering ownership.

Develop and optimize machine learning, computer vision, signal-processing, and image-analysis algorithms for workflow automation and biological technologies. Integrate algorithms with hardware and software systems, optimize deployment on edge devices and GPUs, validate performance, and document results. The role requires strong Python development, mathematical and algorithmic expertise, and collaboration across multidisciplinary engineering teams.

Leads Tala’s Machine Learning Engineering team in building and operating platforms for model development, real-time inference, streaming features, batch processing, deployment, monitoring, and production reliability. Manages and develops 4–6 engineers, owns delivery and prioritization, provides architectural leadership, improves engineering practices and SLOs, and partners with Data Science, Data Engineering, Product, Credit, and Business Development teams.

Leads and develops a perception and machine learning engineering team building computer vision, sensor fusion, tracking, detection, and scene-understanding capabilities for autonomous systems. Owns roadmap planning, technical decisions, delivery, model development and deployment workflows, field readiness, hiring, performance management, and cross-functional execution with autonomy, embedded, data, hardware, product, programs, and field operations teams.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Lead modelling and analysis roadmap for FX products: forecast and monitor FX volatility, optimise trade execution with statistical/ML models, run customer and profitability analyses, and partner with product, trading, risk, and engineering to translate insights into trading and product decisions.

Lead and grow a research engineering team developing conversational AI agents. Own the technical roadmap across model post-training, evaluation, safety, speech, and agent behavior; convert research ideas into measurable production programs; review architecture and experiments; ensure rigorous, reproducible engineering; and partner with infrastructure, platform, product, and customer teams to deploy reliable AI systems.

Own machine learning at Metriport end to end, including problem framing, feature engineering, model training, evaluation, deployment, monitoring, and infrastructure. Build predictive models and document-processing systems from large, messy clinical datasets. Develop training and inference pipelines, experiment tracking, model versioning, and production monitoring. Collaborate with founders, engineers, and customers to identify high-value ML opportunities and deliver reliable healthcare intelligence.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Designs, builds, and operationalizes enterprise AI, generative AI, machine learning, and agentic AI solutions. Develops integrations, orchestration services, reusable tooling, deployment pipelines, monitoring, and governance controls. Partners with architecture, platform, data, and product teams to deliver scalable, secure, compliant AI capabilities across the organization.

Architect data-driven threat-detection strategies, translate complex security threats into mathematical problems, build probabilistic models and graph-analytics detections, and operationalize production-grade machine learning to minimize alert fatigue across the security organization.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

Owns end-to-end development of datasets, reinforcement learning environments, and evaluations for coding agents and software engineering models. Leads engineers, researchers, QA professionals, and data creators; establishes quality processes, pipelines, validation, and review workflows. Collaborates directly with frontier AI labs, fine-tunes models, builds benchmarks, develops automation and internal tools, and supports customer-facing enablement. The role combines hands-on technical leadership, engineering management, operational execution, research, and industry thought leadership.

Design and build scalable ML infrastructure and backend systems for high-impact marketplace applications. Lead architecture, solve low-latency and high-correctness problems, develop and adopt engineering frameworks, and create production-ready technical roadmaps. Partner with Product, Science, and Operations while mentoring engineers and improving engineering standards. The role requires experience with ML models, foundational models or LLMs, MLOps, Kubernetes, data pipelines, distributed workflows, and large-scale scheduling.

Lead the technical strategy and architecture for foundation models powering search, recommendations, personalization, and conversational AI across Uber’s Mobility and Delivery products. Own cross-team initiatives involving retrieval, ranking, embeddings, and LLM assistants; guide build-versus-buy decisions; influence product strategy; deliver large-scale ML systems; and mentor senior engineers while connecting model improvements to business outcomes.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Collaborate with backend engineers to productionize models and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve business problems.

Defines the multi-year technical vision and architecture for foundation models supporting search, recommendations, personalization, and conversational AI. Leads high-impact ML platform initiatives across Mobility and Delivery, drives retrieval, ranking, and LLM system design, and makes build-versus-partner investment decisions. Provides principal-level technical leadership, mentors senior engineers, aligns executives across organizations, and connects model improvements to measurable business outcomes at global scale.