Top AI & Machine Learning Jobs in San Francisco, CA

Leads an enterprise data science team developing agentic AI and machine learning products. Responsibilities include designing, training, validating, and deploying models; conducting A/B tests and telemetry analysis; analyzing large-scale numeric and textual datasets; translating findings into business strategy; partnering with data scientists, engineers, and product managers; and guiding C-suite decision-making and talent development.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, safety, and reliability. Establish long-term platform strategy and company-wide AI standards while mentoring technical leaders and driving cross-functional adoption of responsible AI infrastructure.

Design and lead scalable AI platforms and production systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Establish AI architecture, optimization, safety, and transparency standards; drive long-term infrastructure initiatives; integrate research into reliable products; and mentor senior technical leaders across engineering, research, and data disciplines.

Lead the architecture, development, deployment, and optimization of large-scale AI systems, including foundation models, LLM inference, agents, vector search, guardrails, evaluation, governance, and observability. Define enterprise AI architecture and long-term platform strategy while improving cost, latency, throughput, safety, and reliability. Lead multi-year initiatives, establish responsible AI standards, collaborate across research and product teams, and mentor senior technical leaders.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model evaluation. Establish enterprise AI architecture standards, improve system cost, latency, throughput, and scalability, and promote responsible AI practices. Lead technical direction, cross-functional collaboration, mentoring, research-to-production efforts, and multi-cloud AI strategies.

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

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead multi-model orchestration, champion responsible AI, and mentor engineering teams. Partner with research, product, and program teams to advance AI capabilities from experimentation through production.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model governance. Define enterprise AI architecture and long-term platform strategy while improving performance, cost, scalability, latency, and reliability. Lead AI safety and transparency standards, drive organization-wide platform initiatives, collaborate with cross-functional teams, and mentor senior engineering and research leaders.

Design, develop, deploy, and optimize enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish AI architecture standards, improve model performance and efficiency, lead multi-cloud strategies, and mentor engineering teams while promoting responsible AI practices.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, lead technical reviews and AI governance, contribute to long-term platform strategy, and mentor senior AI engineering leaders.