Top Tech Jobs & Startup Jobs in San Francisco, CA

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.

Lead engineering-driven growth by designing, shipping, and scaling experimentation and A/B testing frameworks for high-traffic AI products. Partner with product, data science, design, and research to turn hypotheses into measured rollouts, improve experimentation quality, and scale successful patterns across global AI services.

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, implement, and debug low-level software for Azure HPC/AI VMs, focusing on hardware/software interactions, device virtualization, and GPU workload performance. Drive architecture, collaborate with partners, own reliability as an on-call DRI, and develop operational playbooks to improve stability and performance.

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.

Design, implement, and optimize high-performance, low-latency networking infrastructure for large-scale AI training on Azure. Benchmark, debug, and scale networking systems for petabyte-scale transfers, create design documents, write and refactor code, act as DRI for services, participate in on-call monitoring, and improve observability and reliability for distributed AI workloads.