Top AI & Machine Learning Jobs in San Francisco, CA

Design and build large-scale, highly available distributed cloud AI services focused on Responsible AI. Lead cross-functional projects, evaluate architectures, own end-to-end development and production readiness, drive DevOps practices, and integrate with Azure AI offerings to ensure performance, scalability, observability, and customer-focused solutions.

Develop machine learning techniques for AI safety, alignment, and trustworthy AI. Collaborate with research, product, and engineering teams to design, build, deploy, monitor, and continuously improve responsible AI systems and LLM-based services at scale.

Design and build foundational AI-first growth and experimentation systems: telemetry, experiment assignment, feature flighting, rollout automation, and risk controls. Lead cross-team initiatives, partner with Product/Data Science/Design, write production code, and raise engineering standards for measurement, experimentation quality, and safe progressive delivery.

Design, fine-tune, and evaluate large language models (including RLHF). Build adversarial agent evaluations and safety mitigations, collaborate with engineering to deploy Responsible AI solutions at scale, and troubleshoot end-to-end operational issues.

Drive technical pre-sales for cloud and AI application solutions: run demos, PoCs, hackathons, and architecture workshops; design secure, scalable cloud-native architectures; unblock technical issues; and evangelize Microsoft AI Foundry, Azure AI, and Responsible AI to accelerate customer adoption and deployments.

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.

Lead technical strategy and execution with strategic partners across ASEAN to build repeatable Microsoft Cloud and Data & AI offerings. Guide architecture, run innovation workshops, enable partner specializations, validate offers, and drive co-sell execution to convert technical work into measurable revenue and consumption.

Designs secure, scalable AI and cloud architectures for Microsoft partners. Leads architecture sessions, develops proofs of concept and pilots, provides technical implementation guidance, supports legacy-to-cloud transitions, and helps partners build production-ready Copilot, agentic, and managed-service solutions. Serves as a technical bridge between Microsoft engineering and strategic partners while advising on data, applications, APIs, security, governance, and enterprise AI adoption.

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.

Develop and iterate post-training recipes for large language models: drive data collection and acquisition, build capability evaluations, design experiments, and apply reward modeling and RL to improve model performance for reasoning, instruction following, code, and tool use.