Top AI & Machine Learning Jobs in San Francisco, CA

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.

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, 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.

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 and evaluate multimodal foundation model architectures, design datasets and pipelines, run large-scale experiments, improve training/deployment efficiency, collaborate with infrastructure and product teams, and ensure responsible, safety-aligned AI development.

Design and implement algorithms, model architectures, data mixtures, and scaling laws for large-scale pre-training. Run and oversee flagship training experiments on distributed infrastructure, collaborate with infrastructure/data/post-training teams, and iterate using rigorous, data-driven ablations to advance foundation models.

Lead end-to-end delivery of high-impact data science and AI projects: translate business needs, prepare data, build and deploy predictive, prescriptive, and generative AI solutions (including LLM fine-tuning and prompt engineering), ensure production readiness and responsible AI, and advise stakeholders to drive adoption.

Design, curate, and evaluate high-quality text datasets for pretraining LLMs. Build scalable ingestion, preprocessing, filtering, and annotation pipelines. Run data ablations and analyses to assess dataset impact, develop auditing and versioning tools, and collaborate with Safety, Ethics, and Governance to ensure responsible, privacy-preserving datasets.

Build and operate large-scale reinforcement learning and pretraining systems: design and implement distributed, parallel, and GPU-optimized software; benchmark NVIDIA and AMD GPU clusters; inform compute roadmap; debug cross-system performance bottlenecks; contribute to generative and conversational AI model development and product deployment.

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.