Top AI & Machine Learning Jobs in San Francisco, CA

Lead end-to-end data science projects in a forward-deployed model: acquire and prepare messy datasets, develop and evaluate production-ready models tied to business outcomes, run experiments, drive ethics/privacy discussions, define solution strategy, and coach cross-disciplinary teams while applying latest AI approaches.

Lead end-to-end data science delivery embedded with customers: acquire and prepare messy data, develop and evaluate production-grade models tied to business outcomes, run experiments, drive ethics/privacy discussions, coach cross-functional teams, and define solution and customer strategy while applying cutting-edge AI and scalable operational modeling.

Enable frontier AI models on Microsoft's Maia AI accelerators by integrating model architectures, optimizing performance, improving serving capabilities, and building validation and automation infrastructure. Diagnose issues across software, systems, and hardware layers; support hardware bring-up; and partner with model, runtime, infrastructure, and hardware teams to deliver reliable, scalable production AI workloads.

Define technical strategy for enabling AI models and hardware generations on Microsoft's Maia accelerators. Lead cross-stack investigations across models, serving systems, infrastructure, and hardware; improve performance, scalability, reliability, validation, and automation; and align teams on durable platform capabilities and long-term technical investments.

Leads enterprise-scale data, AI, and cloud transformation initiatives for federal customers. Designs secure, compliant Azure architectures using generative AI, agentic systems, RAG, document intelligence, and workflow automation. Advises executives, leads architecture workshops, manages technical strategy and risk, supports pre-sales and account growth, and drives customer adoption and measurable value. Develops reusable intellectual property, reference architectures, evaluation frameworks, and accelerators while mentoring architects and technical teams.

Lead product and program analytics for Microsoft skilling experiences: analyze learner journeys and funnels, define success metrics, build measurement frameworks, run experiments and causal analyses, surface opportunities to improve engagement and proficiency, shape telemetry and scalable reporting with engineering, develop competency and skill taxonomies, mentor data scientists, and ensure analyses meet privacy requirements.

Lead design and implementation of ONNX, ONNX Runtime, and on-device inference (Foundry Local). Provide technical leadership, write design documents, mentor engineers, own cross-product dependencies and release plans, act as DRI and on-call for reliability, and drive performance, observability, and operational consistency at scale.

Work on end-to-end performance of LLMs: benchmark and optimize inference on Nvidia/AMD GPUs and Microsoft silicon, build tooling for profiling and deployment, accelerate model porting, and collaborate with internal and external partners to reduce hardware footprint and improve time-to-market.

Develop and operate production-quality AI framework, runtime, benchmarking, performance, automation, and observability systems. Benchmark, profile, debug, and optimize large language model workloads across GPUs and Microsoft hardware. Lead cross-stack investigations, improve model onboarding, reliability, hardware utilization, and Azure capacity efficiency, while collaborating with researchers, infrastructure teams, hardware partners, and engineers. Principal-level responsibilities include setting technical direction, defining architectures, leading strategic initiatives, and mentoring technical leaders.

Build and operate AI-native systems for Azure capacity infrastructure at hyperscale. Responsibilities include designing distributed architectures, developing LLM applications and AI agents, writing secure full-stack code, automating deployment, ensuring reliability and observability, supporting on-call operations, applying security-as-code, and collaborating across product, program, and security teams. The role also involves technical leadership, code reviews, mentoring, and delivering production systems using Azure services, Python, Angular, C#/.NET, and SQL.