Top Machine Learning Jobs in San Francisco Bay Area, CA

Research Engineer responsible for developing, training, evaluating, and deploying healthcare AI models and pipelines for conversational voice agents. The role focuses on instruction following, tool calling, retrieval, memory, speech and audio research, model optimization, benchmarking, failure analysis, and production integration. Candidates should have 5+ years of experience, including AI/ML engineering or research, production LLM deployment, Python proficiency, and a record of converting research prototypes into reliable measurable improvements.

Develop and deploy applied ML and computer vision models to understand technical drawings and specifications. Work on multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to build customer-impacting solutions.

Own backend architecture, code, deployments, monitoring, and on-call for AI shopping agents that perform live purchases. Build resilient checkout automation, scalable product ingestion infrastructure, embedding-based search and ranking, and systems that automatically detect and recover from failures. The role requires strong Python, end-to-end production ownership, and comfort with ambiguous, adversarial systems. Experience with AI agents, LLMs, machine learning, e-commerce, payments, or fraud infrastructure is preferred.

Develop machine learning, optimization, causal inference, and statistical methodologies for Uber marketplace teams. Design experiments, build predictive models, define success metrics, analyze large datasets, and deliver actionable insights for product, growth, pricing, logistics, membership, mapping, and experimentation initiatives. Partner cross-functionally to drive data-informed product development and own science aspects of the product lifecycle.

Productionize computer vision and 3D reconstruction systems for embodied AI. Own spatial output quality, robustness, evaluation infrastructure, performance, throughput, cost, and reliability. Diagnose real-world failures involving calibration, scale, coordinate frames, and scene complexity. Collaborate with research, product, engineering, and customers to deliver production-ready capabilities, workflows, and interfaces for simulation, training, evaluation, and deployment.

Leads and manages ML engineers working across computer vision, robotics, agentic workflows, and ML platform infrastructure. Owns technical direction, hiring, team development, roadmap commitments, production deployment, monitoring, incident response, reliability, and cross-functional collaboration with product and software engineering teams. This player-coach role requires hands-on oversight of production ML systems, platform architecture, evaluation, safety, and operational quality.

Analyze large-scale autonomous driving and simulation data to develop safety and driving-quality metrics, statistical models, predictive features, and machine-learning methodologies. Build repeatable data analysis and reporting pipelines in collaboration with engineering teams. The role requires pursuing a quantitative PhD, programming proficiency in Python or R, and strong statistical theory. Preferred qualifications include machine learning, SQL or C/C++, high-dimensional data experience, research publications, foundation-model expertise, and autonomous-driving or generative-AI internship experience.

Develop AI and machine learning systems for retailer acquisition, engagement, paid marketing optimization, bidding, targeting, AEO content generation, and personalized landing experiences. The role includes causal inference, experimentation, marketing capital allocation, behavioral insights, and scalable solutions for two-sided marketplace challenges. It requires cross-functional collaboration, independent ML solution design, and strong programming and communication skills.

Own end-to-end applied machine learning projects for catalog and listing quality. Build multimodal deep learning and LLM systems to analyze product images, titles, descriptions, and attributes; improve imagery, ranking, exploration, and product information; and develop evaluation workflows involving human feedback. Partner cross-functionally to ship models, measure business impact, and improve discovery and conversion across Faire’s two-sided marketplace.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Leads the end-to-end design, deployment, and performance of real-time ML prediction and bid-optimization systems for programmatic advertising. Provides hands-on technical leadership to applied scientists and ML engineers, formulates optimization problems, manages production ML challenges such as latency, delayed feedback, drift, and retraining, and aligns technical roadmaps with business priorities.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Lead advanced analytics initiatives by translating ambiguous business problems into analytical solutions, building and deploying predictive and machine learning models, and delivering actionable insights. Partner with stakeholders across sales, finance, supply chain, eCommerce, cloud, and vendor management. Independently scope workstreams, shape technical roadmaps, lead cross-functional collaboration, and communicate complex findings through executive-ready presentations.

Leads the technical direction and end-to-end delivery of machine learning and LLM systems for merchant quality, integrity, relevance, and shopping discovery. Responsibilities include architecture, implementation, evaluation, experimentation, monitoring, safety, scalability, and production rollout. The role establishes ML engineering standards, partners cross-functionally, mentors engineers, influences technical roadmaps, and supports future hiring while improving user trust and shopping outcomes.

Own and evolve Braze’s ML platform for production-scale training, deployment, serving, observability, reliability, and cost efficiency. Lead complex infrastructure initiatives, including multi-region model serving, customer-specific model pipelines, CI/CD tooling, orchestration, and cloud identity. Set technical direction, manage incidents, collaborate across teams, improve engineering quality, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Own the strategy, design, development, and deployment of AI safeguards systems and product experiences across Anthropic’s products and cloud platforms. Define safety-by-design approaches, develop evaluations and risk metrics, prioritize roadmaps, make technical tradeoffs, and collaborate with policy, research, engineering, enforcement, and product stakeholders to mitigate deployment and user risks.

Conduct applied AI research focused on computer vision, multimodal, robotics, and coding models. Design datasets, evaluations, benchmarks, and reinforcement learning environments; analyze model failures; develop data remediation specifications; validate data quality through fine-tuning and ablation studies; contribute to open models; publish research; and collaborate with frontier labs to translate model roadmaps into data programs.

Conduct research advancing assurance methods for embodied AI systems, including robots, drones, and autonomous vehicles. Develop prototypes to evaluate safety and reliability methods, define research directions, collaborate with universities, publish scientific findings, and present work at conferences. The role requires machine learning model development, digital tool implementation, and clear communication of complex concepts.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Build and own production AI features for permitting workflows, including document extraction, retrieval, agentic systems, evaluation, observability, deployment, and iteration. Define quality standards, improve LLM reliability, make product and architecture decisions, mentor engineers, and work directly with users. The role requires substantial production LLM experience and in-person work four days weekly in the San Francisco Bay Area.

Conduct independent data science research and own an end-to-end project involving product, user, or business data. Responsibilities include framing ambiguous questions, analyzing data, designing experiments, developing statistical or machine learning models, communicating findings, and influencing product or business decisions. Projects may involve causal inference, AI/LLM evaluation, recommendation systems, search, personalization, or user research, with potential for publication.

Own Pulley’s AI technical direction and production LLM systems for permitting intelligence. Build document extraction, classification, retrieval, and agentic workflows; establish evaluation and observability standards; make model, architecture, cost, and latency decisions; and mentor engineers. The role requires hands-on production experience with LLMs, AI coding agents, reliability engineering, and end-to-end ownership of AI domains, with possible work on OCR, document understanding, fine-tuning, and full-stack AI features.

Build and deploy production AI, machine learning, and computer vision systems for aircraft manufacturing. Responsibilities include developing AI-enabled services, APIs, data pipelines, visual inspection and anomaly detection solutions, model safeguards, monitoring, and integrations with factory systems and equipment. The role owns end-to-end delivery, leads technical design, mentors engineers, and establishes scalable practices for reliable AI deployment.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Conduct research and develop mathematically rigorous algorithms for real-time auctions, dynamic pricing, bid shaping, pacing, traffic allocation, and fraud detection. Apply machine learning, reinforcement learning, online learning, game theory, forecasting, and Bayesian methods. Deploy models at massive scale, run rapid experiments, evaluate long-term system behavior, collaborate with product and engineering teams, and contribute through publications and internal research seminars.