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

Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.

Lead the design and delivery of knowledge graph and machine learning systems that infer personal work context from connected tools. Build graph inference pipelines, schemas, permission-aware APIs, Rovo Chat integrations, and MCP-compatible CLI experiences. Establish evaluation frameworks, improve retrieval and grounding, provide technical direction across teams, mentor engineers, and ensure responsible, privacy-safe AI systems operate reliably at production scale.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

Lead Cisco’s agentic AI evaluation strategy from architecture through production. Design layered evaluation systems using automated checks, LLM judges, human calibration, simulation, synthetic data, and adversarial testing. Monitor live traffic for regressions and drift, convert production traces into test cases, and validate fixes through shadow and A/B testing. Establish measurement standards across teams, manage datasets and experiment tracking, mentor engineers, and represent AI evaluation practices to senior leadership.

Lead the technical vision and long-term machine learning strategy for Consumer Incentives. Design personalization, causal measurement, experimentation, and constrained optimization solutions; architect scalable end-to-end ML systems; evaluate reinforcement learning, foundation models, and generative AI; and drive cross-functional initiatives from formulation through production. Mentor technical leaders, establish engineering best practices, and connect model improvements to measurable consumer and business outcomes.

Lead the technical direction for Uber’s rider pricing machine learning systems. Develop and improve pricing and promotion-targeting models, optimization algorithms, and production serving systems. Own technical roadmaps, collaborate cross-functionally, mentor engineers, and lead teams delivering scalable real-time and offline solutions. Apply deep learning, generative AI, causal modeling, personalization, and reinforcement learning to improve ridership, revenue, and profitability.

Lead machine learning and optimization initiatives for Uber’s rider pricing systems. Develop, improve, evaluate, and deploy scalable pricing and promotion-targeting algorithms. Own technical problems end to end, shape product and technical roadmaps, collaborate across ML, serving, and product teams, and mentor engineers. Apply deep learning, generative AI, causal modeling, representation learning, personalization, and reinforcement learning to improve pricing, affordability, ridership, and revenue.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

Lead the architecture and development direction of NVIDIA ALCHEMI and related AI software for chemistry and materials science. Design developer tools, APIs, and workflows; translate advances in machine learning, generative modeling, interatomic potentials, and quantum chemistry into product capabilities; coordinate researchers, engineers, product teams, partners, and customers; and represent NVIDIA at scientific and industry events.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Own and advance production search, recommendation, retrieval, and ranking systems for a consumer social and interactive entertainment platform. Build candidate generation, embedding retrieval, two-tower models, ranking features, user and content representations, and online serving systems. Lead experimentation and evaluation to improve discovery, engagement, retention, personalization, cold-start performance, and content distribution while partnering with product, data, and engineering teams.

Leads the technical direction, architecture, and roadmap for agentic machine learning systems processing global trade data. Builds ingestion, document understanding, entity resolution, inference, evaluation, observability, and feedback systems. Adapts and post-trains LLMs, ships production AI agents, mentors engineers, sets technical standards, and partners with product, engineering, data science, and customers to deliver risk, compliance, and enforcement capabilities.

Lead the technical direction and development of production machine learning and agentic systems for global trade risk, compliance, and enforcement. Responsibilities include designing data-to-decision pipelines, adapting and post-training LLMs, building evaluation and observability systems, improving model performance, mentoring engineers, and partnering with product, customers, and cross-functional teams to ship AI capabilities.

Lead the technical strategy and development of machine learning solutions for Uber’s homepage personalization, including sequential recommendation, intent-aware ranking, LLM-based systems, and agentic AI. Partner with product teams to improve engagement and business outcomes, influence technical direction across teams, and mentor machine learning engineers.

Build and scale end-to-end machine learning infrastructure for computer vision and multimodal AI systems. Responsibilities include developing training and deployment pipelines, managing GPU cloud infrastructure, optimizing low-latency model serving, and establishing monitoring and evaluation frameworks for reliable production performance. The role partners with research, computer vision, and product teams to operationalize models across warehouse environments.

Design, build, deploy, and optimize large-scale machine learning models and infrastructure for Uber Maps, including high-throughput, low-latency prediction systems and APIs. Develop distributed ML pipelines, improve routing and ETA applications, explore optimization solutions, and provide technical leadership. Collaborate with product managers, scientists, designers, and engineers to solve complex, real-world mobility problems.

Lead applied AI and machine learning research for Pinterest’s generative recommendation and reinforcement learning systems. Architect, build, and deploy novel ML solutions at scale, collaborate across product, engineering, and data science, identify research opportunities, shape long-term strategy, mentor others, and translate advanced research involving recommendation systems, LLMs, and VLMs into production impact.

Build and operate production trust and safety systems that detect and disrupt platform abuse at internet scale. Responsibilities include developing ML infrastructure, training and feature pipelines, serving systems, evaluation frameworks, and LLM/classical ML detection solutions. Own systems from model integration through deployment, monitoring, and iteration while collaborating with security, product, and infrastructure teams.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

As a Senior Scientist, you will develop machine learning methods for biologics design, optimize large-molecule therapeutics, and work collaboratively with experimental teams to enhance drug development processes.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Develop and deploy state-of-the-art machine learning methods for multimodal human health data, including imaging, EHRs, biomarkers, longitudinal outcomes, genetics, and omics. Build precise phenotypes, model patient states, predict clinical endpoints, identify therapeutic targets, and develop biomarkers. Create reusable production ML components while collaborating with machine learning scientists, statistical geneticists, life scientists, software engineers, and clinicians.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.