Top Machine Learning Engineer 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.

Design, implement, and integrate machine learning models and data pipelines for a programmatic DSP. Support model development for prediction, forecasting, and fraud detection; prepare large datasets; collaborate with cross-functional teams; document experiments and maintain reproducibility.

Run and evaluate machine learning training experiments for autonomous robots, curate and clean real-world fleet data, improve data-labeling structures, track model performance metrics, and collaborate with Teleoperations to improve data collection. The role focuses on production ML for perception, decision-making, and control, including imitation learning, reinforcement learning, and deployment evaluation on live robotic systems.

Own end-to-end machine learning model quality for autonomous robots, including dataset curation, training, evaluation, and production deployment. Work with multimodal sensor data from cameras, IMUs, LiDAR, and odometry to develop perception, planning, navigation, and manipulation models. Build low-latency, continuous inference systems and collaborate with teleoperations and fleet operations teams. Experience with large-scale multimodal models, distributed GPU training, production ML code, and cluster management is valuable.

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.

Senior AI/ML Engineer responsible for translating federal supply chain and logistics problems into analytical solutions, developing forecasting, classification, and NLP models, and deploying them into production. The role owns model development, data pipelines, monitoring, retraining, drift detection, validation, documentation, and stakeholder communication. Work includes operating within government-controlled environments and meeting federal bias evaluation and documentation requirements.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

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.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Builds benchmarks and evaluation systems for AI models across domains and modalities. The role owns backend infrastructure, data pipelines, execution environments, storage, orchestration, and sandboxed agentic evaluation systems. It also leads customer-facing technical engagements, translating enterprise requirements into scalable production solutions, collaborating with researchers, and driving ambiguous projects from feasibility through delivery.

Build and deploy production AI/ML and GenAI systems across the full model lifecycle. Develop RAG pipelines, LLM integrations, data pipelines, labeling workflows, evaluation frameworks, and MLOps infrastructure. Apply AI to manufacturing, computer vision, predictive maintenance, supply chain, forecasting, inventory, supplier risk, and logistics. Collaborate with engineering, manufacturing, and operations teams to deliver measurable business outcomes.

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.

Design, build, and deploy large-scale ML models powering core products; own full ML lifecycle from data analysis to production; partner cross-functionally to prototype and launch features; apply modern ML techniques and AI tooling while ensuring code quality, security, and scalable production services.

Lead the design, development, and deployment of AI software components while partnering with engineers and scientists to deliver innovative AI solutions at Capital One.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Develop and optimize large-scale machine learning models, open-source LLM infrastructure, and training and inference systems for advertising applications. Build distributed data and model pipelines, adapt DeepSpeed and OpenRLHF, optimize GPU/TPU serving for latency and cost, monitor model performance, and collaborate with applied scientists on research. The role focuses on recommender systems, personalization, targeting, reinforcement learning, and ad intelligence at massive 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.

Build and deploy production AI/ML features across the model lifecycle, including GenAI systems, RAG pipelines, data pipelines, evaluation infrastructure, and MLOps. Apply machine learning to manufacturing, supply chain, computer vision, predictive maintenance, forecasting, inventory, and logistics problems. Collaborate with engineering, manufacturing, and operations teams to deliver measurable outcomes.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy-farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and integration with cloud infrastructure. The role also drives AI adoption, establishes engineering practices, collaborates cross-functionally, and may build React and TypeScript user experiences.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.