Top AI & Machine Learning Jobs in San Francisco, CA

Design, develop, deploy, optimize, and support foundational AI systems, including LLM inference, model training, agentic workflows, vector search, guardrails, and multi-model orchestration. Lead cost-performance governance, technical design reviews, AI standards, and roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference cost. Mentor senior technical leaders and collaborate with engineers, scientists, product managers, and program managers.

Design, develop, deploy, and operate machine learning models and components at scale. Build data pipelines, cloud-based ML architectures, production services, and automated testing and deployment workflows. Collaborate with Product and Data Science teams, retrain and monitor production models, optimize algorithms and infrastructure, and apply responsible AI, governance, security, and explainability practices.

Design, build, deploy, monitor, and maintain machine learning models and production systems. Develop optimized data pipelines, cloud-based ML architectures, application code, automated tests, and CI/CD processes. Collaborate with Product and Data Science teams on scalable AI solutions, model governance, responsible AI, and production reliability using distributed systems and containerized cloud infrastructure.

Design, develop, deploy, and optimize large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead architecture, cost-performance governance, technical design reviews, and AI engineering standards. Build multi-model orchestration pipelines, optimize hardware utilization and inference efficiency, contribute to long-term platform roadmaps, and mentor senior AI engineering leaders.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, governance, and observability. Optimize model performance, GPU/TPU utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, contribute to platform roadmaps, and mentor engineering associates.

Designs, develops, deploys, and operates machine learning models and production systems at scale. Responsibilities include model development, data pipeline construction, cloud architecture, distributed processing, Kubernetes-based deployment, CI/CD automation, monitoring, retraining, governance, responsible AI, and collaboration with product and data science teams.

Designs, develops, deploys, and supports scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for cost, latency, throughput, reliability, and hardware utilization; owns complex system architecture; defines reliability objectives; leads technical reviews; and mentors engineering colleagues.

Architect, build, and deploy enterprise-scale generative AI and agentic systems for HR technology. Lead development teams and technical initiatives involving full-stack applications, distributed microservices, cloud platforms, LLM workflows, and secure AI implementations. Partner with security, governance, product, and architecture teams to deliver compliant solutions, define service boundaries, establish engineering standards, reduce technical debt, and mentor engineers.

Designs, develops, deploys, and optimizes foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, governance, and observability. The role leads cost-performance optimization, multi-model orchestration, technical standards, design reviews, and mentoring while partnering with engineering, research, program management, and product teams.

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 AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering staff.