Top AI & Machine Learning Jobs in San Francisco, CA

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, ensure security and compliance, contribute to strategic roadmaps, and mentor senior technical staff.

Leads AI engineering strategy, teams, and delivery of scalable foundation models, LLM inference, similarity search, guardrails, evaluation, governance, and observability systems. Oversees technical architecture, optimization, build-versus-buy decisions, responsible AI standards, enterprise roadmaps, and cross-functional execution. Develops talent, establishes engineering practices, and partners with research, compliance, and risk teams to deploy reliable AI products across the organization.

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

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining AI strategy, designing experiments and models, evaluating and validating modeling systems, analyzing telemetry, building benchmark and challenger models, and advising senior business and regulatory stakeholders. The role requires extensive experience with large-scale data analysis, machine learning, relational databases, open-source programming languages, cloud platforms, software architecture, and DevOps practices.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize distributed AI infrastructure for cost, latency, throughput, scalability, and hardware utilization. Own complex system architectures, define reliability objectives, lead technical reviews, ensure security and compliance, shape long-term platform strategy, and mentor senior technical associates.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Define technical roadmaps and standards, lead architecture and governance reviews, integrate heterogeneous AI components, and mentor senior engineering talent.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance reviews, optimize models and infrastructure for latency, throughput, scalability, and cost, establish AI engineering standards, and mentor senior technical teams.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize training and inference for cost, latency, throughput, scalability, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term platform roadmaps, and mentor engineering staff.

Design, develop, deploy, and support scalable generative AI systems, including foundation models, LLM inference, agentic and multi-agent workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, and hardware utilization. Shape technical roadmaps, lead design reviews and governance councils, integrate heterogeneous AI pipelines, and mentor senior AI engineers while applying responsible AI standards.

Design, develop, deploy, and support scalable AI systems involving foundation models, LLM inference, agentic workflows, guardrails, evaluation, similarity search, governance, and observability. Optimize training and inference for cost, latency, throughput, and hardware utilization. Define reliability objectives, lead architectural reviews, ensure security and compliance, contribute to long-term AI platform roadmaps, and mentor engineering staff.