Top AI & Machine Learning Jobs in San Francisco, CA

Design, develop, optimize, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, orchestration, governance, and observability. Lead cost-performance optimization across production AI platforms, establish engineering standards, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineers. Collaborate with research, engineering, program management, and product teams to deliver scalable, responsible AI products.

Leads machine learning engineering teams while designing, deploying, scaling, monitoring, and governing production ML models and platforms. Responsibilities include building distributed ML systems, cloud architectures, data pipelines, CI/CD automation, model retraining, responsible AI practices, and resilient production services. The role combines hands-on technical leadership with mentoring, recruiting, talent development, and cross-functional collaboration with Product and Data Science teams.

Design, develop, deploy, and support foundational AI systems, including 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 system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to strategic roadmaps, and mentor senior technical staff.

Design, develop, deploy, and support foundational AI systems, including foundation model training, 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 AI performance governance, lead design reviews, define technical roadmaps, and mentor senior AI engineering talent.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, orchestration, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Establish AI governance standards, lead technical reviews, shape long-term platform strategy, and mentor senior AI engineers. Collaborate with engineering, research, product, and program teams to deliver responsible AI products.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, orchestration, cost-performance governance, technical reviews, roadmap development, and responsible AI standards. Mentor senior technical staff and apply advanced optimization techniques to improve model scalability, latency, throughput, hardware utilization, and inference cost.

Leads applied AI research and teams developing foundation models and production-scale machine learning systems. Sets research strategy, mentors applied scientists, partners with engineering and product leaders, and translates advanced AI research into customer-facing products. Requires expertise in large-scale deep learning, LLMs, optimization, model deployment, research publications, and organizational leadership.

Build and deploy scalable generative AI platform services, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Leads enterprise AI engineering strategy and multi-team delivery of scalable, responsible AI systems. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Establishes responsible AI standards, makes technology decisions, develops long-term platform roadmaps, partners with research and risk teams, and attracts and mentors engineering talent.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agents, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI cost-performance governance, lead technical design reviews, shape foundational AI roadmaps, and mentor senior AI engineering leaders.