Top AI & Machine Learning Jobs in San Francisco, CA

Customer-embedded principal data scientist who leads account-aligned engagements, translating business needs to production-grade ML/data solutions. Builds and ships end-to-end systems, influences senior stakeholders, accelerates time-to-value, mentors/hand-offs to FDE crews, and navigates complex account dynamics to deliver sustained business impact.

Lead end-to-end delivery of data science and AI engagements: translate business problems into AI solutions, prepare and model large datasets, build and deploy predictive/prescriptive and generative AI applications, implement prompt engineering and fine-tuning for LLMs, present insights to senior stakeholders, and promote responsible, scalable AI practices across teams.

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.

Lead and grow a software engineering team to design, build, and operate high-performance, scalable, and observable networking systems for Azure AI supercomputers. Drive architecture, reliability, telemetry, automation, and cross-team collaboration to support large-scale distributed training and inference workloads.

Senior Software Engineer to design and build next-generation networking infrastructure for large-scale AI training on Azure. Own architecture, produce secure, high-performance code, create test strategies, use AI tools for development and automation, lead code reviews, and collaborate across teams to ensure scalability, reliability, and security.

Design, build, and maintain large-scale data infrastructure and pipelines to ingest and process petabytes of multimodal training data (text, audio, images, video). Own Spark, Ray, vector database systems, collaborate with pre/post-training teams to improve data recipes, and support experimentation to optimize datasets for large AI model training.

Work with researchers and engineers to implement and optimize generative AI inference systems. Build tools for debugging performance, profiling, distributed systems, and improve compute efficiency across large heterogeneous GPU fleets. Deliver end-to-end solutions and processes to accelerate research and production inference.

Design and build large-scale evaluation infrastructure for generative AI on GPU supercomputers. Collaborate with model scientists to implement evaluation methods, benchmark GPU clusters, tune pretraining software for Nvidia and AMD architectures, and drive compute roadmap and architectural improvements to support Copilot and other consumer AI products.

Build and optimize distributed pre-training infrastructure for large-scale GPU clusters: implement and tune training software in Python/C++, profile and benchmark compute/storage/networking, optimize collective communication (NCCL), collaborate with hardware teams, and scale training pipelines to support frontier AI models and products like Copilot.

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.