Top AI & Machine Learning Jobs in San Francisco, CA

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 cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Leads enterprise data science initiatives for agentic AI products, including AI/ML model development, experimentation, telemetry analysis, and strategic recommendations for senior leadership. Partners with data scientists, engineers, and product managers while translating complex statistical insights into business outcomes. Requires extensive experience in quantitative analytics, large-scale data analysis, production AI/ML deployment, A/B testing, and team leadership.

Designs, develops, deploys, and supports large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model evaluation. Defines enterprise AI architecture, optimizes model performance, cost, latency, and throughput, establishes AI safety and governance standards, leads multi-year platform initiatives, and mentors senior technical leaders across engineering and research.

Lead the design, development, deployment, and governance of scalable AI systems, including foundation models, LLM inference, similarity search, guardrails, evaluation, and observability. Define AI platform strategy, optimize production systems, establish Responsible AI standards, and translate enterprise priorities into execution plans. Build and mentor multi-team engineering organizations while partnering with research, product, compliance, and risk teams.

Leads the architecture, development, deployment, and optimization of large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes enterprise AI architecture, performance and safety standards, and multi-year platform strategies. Partners across engineering, research, product, and program management while mentoring technical leaders and driving responsible AI adoption.

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 hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Design, develop, optimize, deploy, and support foundational AI systems, including LLMs, agents, vector search, model evaluation, orchestration pipelines, and responsible AI controls. Lead cost-performance governance, technical design reviews, roadmap development, and optimization of model and hardware utilization. Mentor senior AI engineers while partnering with research, product, program, and engineering teams to deliver scalable production AI platforms.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, guide hybrid and multi-cloud strategies, and promote responsible AI practices. Lead technical direction, mentor engineering teams, collaborate with cross-functional stakeholders, and advance research techniques into production.

Lead the architecture, development, deployment, optimization, governance, and observability of scalable AI platforms and systems. Build foundation model training, LLM inference, agentic workflows, similarity search, guardrails, and evaluation capabilities. Establish enterprise AI safety and transparency standards, drive long-term infrastructure strategy, mentor technical leaders, and guide organization-wide adoption of responsible AI solutions.

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. Define reliability objectives, lead architecture and technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor senior technical staff.