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

Lead the design and delivery of scalable machine learning systems and customer-facing generative AI applications for Rovo Chat. Build agent harnesses that improve quality, reliability, and latency; advance state-of-the-art AI capabilities; lead engineers through technical design and launch; and collaborate cross-functionally with teams and internal customers. The role also involves applying a deep understanding of the machine learning project lifecycle and supporting platform-based product development.

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.

Build and lead production machine learning and evaluation systems for Atlassian’s Rovo AI agents. Responsibilities include developing datasets, simulations, automated judges, experimentation platforms, and quality signals; advancing reasoning, planning, retrieval, memory, and tool use; identifying and correcting systemic failures; and balancing quality, safety, latency, reliability, and cost. The role requires hands-on technical leadership, cross-functional influence, architectural decision-making, mentorship, and delivery of measurable customer outcomes at scale.

Lead the Ranking & Relevance team of software and machine-learning engineers. Own retrieval, ranking, recommendation, and personalization systems that match patients with therapists. Drive the transition from filter-based matching to outcome-aware machine learning, establish shared marketplace objectives, improve experimentation and model monitoring, and build evaluation and drift-detection capabilities. Hire and develop engineers, set technical standards, and partner with product, data science, payer, and provider engineering teams in a sensitive healthcare domain.

Design, build, deploy, scale, and monitor production machine learning systems. Develop models, application code, data pipelines, cloud architectures, automated tests, and CI/CD processes. Collaborate with Product and Data Science teams, ensure high availability and performance, govern models for risk and responsible AI, and lead teams developing ML solutions.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

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.

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.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

Design, productionize, and operate machine learning models and rule-based decision systems for credit underwriting. Build scalable pipelines for feature engineering, training, validation, deployment, monitoring, and continuous improvement. Optimize model performance, collaborate with software, credit, product, risk, and data teams, and promote strong engineering and MLOps practices across production ML systems.

Lead the end-to-end development and operation of machine learning systems supporting customer retention, ranking, matching, recommendations, pricing, and lifetime value growth. Build scalable data pipelines and ML infrastructure across batch and real-time environments, while implementing model monitoring, observability, deployment, and performance optimization. Collaborate across engineering and science teams and promote strong software engineering practices.

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 autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Architect and build scalable engineering systems while developing and optimizing machine learning models for Uber’s delivery marketplace. Lead organization-wide frameworks and engineering projects, solve low-latency and high-correctness problems, oversee incident management, and collaborate with Product, Science, and Operations. Mentor engineers, drive technical roadmaps, improve developer velocity, and support reliable systems at global scale.

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. Partner with backend engineers to deploy models to production and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve complex logistics problems.

Build and operate production machine learning and backend systems for AI data and evaluation products. Own a business vertical end to end, from identifying opportunities and setting direction to delivering revenue-generating products. Partner with customers, product managers, and engineers; drive adoption, customer value, and execution quality. Apply machine learning across multimodal data, LLM evaluation, physical-world data, robotics, audio, video, and related domains.

Lead the technical direction and development of Uber’s real-time marketplace matching optimization systems. Build and productionize machine learning and decision systems using reinforcement learning, deep learning, causal inference, optimization, and control methods. Formulate ambiguous problems, develop behavioral models, create simulation and experimentation frameworks, and deploy scalable online inference systems. Collaborate cross-functionally, lead 0-to-1 initiatives, improve marketplace performance, and mentor engineers.

Lead machine learning engineering for fraud prevention and account integrity. Shape technical roadmaps, formulate problems, design and productionize large-scale ML systems, oversee model pipelines and system architecture, conduct A/B testing, and manage rollouts. Mentor and develop MLE talent while applying machine learning, statistics, and optimization techniques to real-world risk and trust challenges.

Develop and own production machine learning models for Uber One offer targeting, incentive allocation, and personalized membership messaging. Responsibilities include uplift modeling, budget-constrained optimization, ranking and sequencing, experimentation, real-time and batch deployment, monitoring, retraining, and cross-functional translation of business goals into ML solutions.

Build and launch scalable distributed systems and AI-powered products for Uber’s consumer incentives business. Own machine learning projects end to end, including scoping, offline evaluation, experimentation, production deployment, algorithm tuning, and post-launch maintenance. Collaborate with product, operations, and data science stakeholders while optimizing consumer experiences and business outcomes across grocery and food verticals.

Build and scale machine learning and backend distributed systems for Uber AI Solutions. Own an AI product vertical end to end, driving technical direction, customer value, revenue, adoption, and market growth. Partner with product and business leaders, guide strategic customer relationships, make decisions in ambiguous environments, and build high-performing teams. The role focuses on production ML, GenAI and LLM systems, evaluation, multimodal data, and other applied AI domains.

Drive end-to-end ML solutions for Uber Freight: identify opportunities, research techniques, prototype, deploy and monitor production models at scale, and integrate Generative AI/agentic systems to optimize logistics and operations.

Develop and productionize machine learning models for supply chain forecasting and optimization. Responsibilities include creating model architectures, curating proprietary real-world data, integrating LLM reasoning and tool use, designing evaluations, and continuously improving performance as market conditions change. The role owns the full research cycle from hypothesis through production and requires collaboration in a fast-moving, in-person environment.

Design, build, and deploy core ML systems and components for an AI agent platform, including search ranking, proactive suggestions, orchestration, routing, and planning; integrate cutting-edge LLMs and ensure high-quality user experiences while working autonomously in a fast-paced, collaborative environment.