Top AI & Machine Learning Jobs in San Francisco, CA

Develop and lead production AI infrastructure across frameworks, runtimes, benchmarking, performance tooling, observability, and hardware platforms. Responsibilities include optimizing large language model training and inference, building automation, diagnosing cross-stack performance issues, defining technical architecture, driving platform improvements, collaborating across research and infrastructure teams, and mentoring engineers. The role spans senior and principal-level individual contributor responsibilities focused on scalable, reliable, and efficient AI systems.

Lead architecture and hands-on implementation for AI frameworks, performance systems, benchmarking, compilers, runtimes, and developer tools. Drive cross-team initiatives that improve LLM onboarding, inference performance, reliability, hardware utilization, and Azure capacity efficiency. Conduct complex systems investigations, establish scalable measurement and observability, influence technical strategy, mentor engineers, and deliver reusable production platforms across models, infrastructure, and silicon.

Own end-to-end data science and AI projects, translating business needs into predictive, prescriptive, and generative AI solutions. Responsibilities include data preparation, model development and deployment, prompt engineering, fine-tuning and evaluation of large language models, stakeholder communication, consulting, responsible AI practices, and documenting best practices.

Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for high-performance LLM serving. Profile workloads, improve latency, throughput, reliability, and hardware efficiency, and integrate optimizations into production systems. Principal-level engineers lead cross-stack performance initiatives and create reusable capabilities across models and hardware. The role also involves collaboration across model, infrastructure, research, and hardware teams, technical mentoring, and raising engineering standards.

Design, implement, test, and operate production AI framework, runtime, benchmarking, performance, automation, observability, and developer-tooling components. Benchmark and optimize large language model training and inference across GPUs and Microsoft hardware. Diagnose cross-stack performance and reliability issues, drive projects through production deployment, collaborate with research, infrastructure, model, and hardware teams, and mentor engineers.

Customer-facing Cloud Solution Architect driving AI transformation with Microsoft Copilot (Chat, Agents, M365). Advise customers, design architectures, enable adoption, build agentic AI solutions, collaborate with sales and technical teams, and create reusable IP to accelerate business value and sustained usage.

Drive technical sales and adoption of Microsoft AI and cloud platforms through PoCs, demos, hackathons, and architecture workshops. Design secure, scalable AI application architectures, resolve technical blockers, collaborate with engineering, and provide developer-facing thought leadership to accelerate production deployments and developer productivity.

Design and lead enterprise data and GenAI architectures, translate business outcomes into scalable secure solutions, drive migration/modernization, define GenAI/Copilot/RAG patterns, establish MLOps/LLMOps, ensure Responsible AI and governance, and act as trusted technical advisor across presales to production.

Build and maintain large-scale multimodal infrastructure supporting data processing, model pretraining, post-training, inference, and serving. Collaborate with research scientists and product engineers to solve infrastructure challenges, optimize distributed CPU/GPU workloads, influence software and hardware architecture, and deliver AI capabilities rapidly. Work spans multimodal data, generative models, distributed training, evaluation, alignment, reinforcement learning, and production-scale serving.

Build scalable infrastructure that transforms first- and third-party data, model signals, evaluations, and synthetic data into governed training datasets for frontier AI models. Responsibilities include data ingestion, curation, provenance, licensing, privacy, access control, policy enforcement, quality measurement, synthetic data generation, failure mining, and evaluation-to-training feedback loops. The role requires Python, SQL, and distributed data-processing experience using Spark, Flink, or Ray.