Top AI & Machine Learning Jobs in San Francisco, CA

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish cost-performance governance, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineering talent.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for scalability, latency, throughput, hardware utilization, and cost. Lead orchestration architecture, governance reviews, design councils, technical roadmaps, and mentorship while integrating heterogeneous AI systems into responsible production pipelines.

Conduct applied research and build AI foundation models from design through training, evaluation, validation, and production implementation. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research areas may include deep learning, graph and sequential models, model fine-tuning, data preparation, robustness, explainability, and reinforcement learning. Translate advanced research into business outcomes and production systems.

Lead the design, development, deployment, and support of foundational AI infrastructure, including model access, LLM inference, similarity search, guardrails, evaluation, governance, observability, and optimization. Make build-versus-buy decisions, establish the technical roadmap, improve scalability, latency, throughput, and cost efficiency, and lead teams of engineers and AI professionals. Develop talent, promote responsible AI practices, and apply emerging research to production systems.

Design, develop, deploy, and support foundational AI systems, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Apply advanced optimization techniques to improve scalability, latency, throughput, cost, and hardware utilization. Partner with research, engineering, product, and program teams to deliver responsible AI products, shape technical strategy, and mentor engineers.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize AI training and inference for performance, latency, throughput, scalability, and cost. Shape the technical roadmap for foundational AI systems, apply emerging research in production, and lead cross-functional collaboration and engineering teams.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Collaborate with engineers, research scientists, technical program managers, and product managers; contribute to technical vision and roadmap; and lead and mentor engineering teams.

Designs, develops, tests, deploys, and supports production AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Develops optimization techniques to improve AI system scalability, latency, throughput, cost, and hardware utilization. Partners with engineering, research, product, and program teams; contributes to foundational AI strategy and roadmaps; and mentors engineering teams and influences senior stakeholders.

Designs, builds, tests, deploys, and supports AI software components for large-scale production systems. Focus areas include foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and performance optimization. Partners with engineering, research, TPM, and product teams to deliver responsible, scalable AI capabilities and shape long-term platform strategy.

Lead machine learning engineering for production ML systems, including model design, code development, cloud-based architectures, and scalable data pipelines. Partner with Product and Data Science teams, retrain and monitor models in production, automate testing and deployment, and apply Responsible and Explainable AI practices.