Top AI & Machine Learning Jobs in San Francisco, CA

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, software engineers, machine learning engineers, and product managers to deliver scalable AI products. Research advanced methods involving language, vision, graphs, optimization, robustness, explainability, and reinforcement learning. Produce technical reports and conference submissions while independently pursuing impactful research agendas and delivering models, libraries, and platform code to production.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, agentic workflows, vector search, model evaluation, governance, and observability. Lead optimization of production AI systems for cost, latency, throughput, and scalability. Define technical roadmaps and standards, conduct governance reviews, architect multi-model orchestration pipelines, lead design councils, and mentor senior AI engineering personnel.

Build and optimize foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance optimization for scalable production AI platforms, integrate heterogeneous models, establish engineering standards, and mentor senior technical staff. Collaborate with research, engineering, product, and program teams to deliver responsible AI capabilities.

Design, develop, deploy, and support foundational AI systems, including LLM inference, foundation model training, 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, lead technical design reviews, establish performance governance, define AI engineering standards, and mentor senior AI engineers.

Conduct applied AI research and develop foundation models from design through deployment. Partner with data scientists, engineers, and product managers to deliver scalable AI products. Lead research initiatives involving large-scale deep learning, model optimization, self-supervised learning, robustness, explainability, fine-tuning, and data preparation. Translate research findings into production systems, guide junior team members, and independently pursue impactful research agendas.

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

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, latency, throughput, cost, and hardware utilization across production platforms. Own complex AI architecture, reliability objectives, security, and compliance reviews while contributing to technical roadmaps and mentoring engineering colleagues.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, guardrails, model evaluation, similarity search, governance, and observability. Optimize large-scale model training and inference for cost, latency, throughput, and hardware utilization. Define AI reliability objectives, lead technical reviews, ensure security and compliance, shape platform roadmaps, and mentor engineering associates.

Design, build, 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 latency, cost, throughput, scalability, and hardware utilization. Define reliability objectives, lead architecture and technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering associates.

Design, develop, deploy, and optimize responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead orchestration and cost-performance governance for large-scale production AI platforms, establish engineering standards, contribute to technical roadmaps, and mentor senior AI engineers. Collaborate with research, engineering, product, and program teams to deliver scalable AI capabilities.