Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Build and own internet-scale web-crawling and ingestion systems for pretraining data. Responsibilities include designing distributed crawlers, extraction and deduplication pipelines, data-quality filtering, specialized crawlers, and petabyte-scale infrastructure. The role partners with pretraining teams to assess model impact, improves system reliability and efficiency, and helps define technical direction. Candidates need extensive experience with web crawlers or large-scale data acquisition, distributed systems, and the practical and legal aspects of web data collection.

Design, build, and operate secure sandboxing infrastructure for untrusted, model-generated code and tool calls. Responsibilities include improving isolation with containers and microVMs, developing scheduling and resource-management systems, scaling concurrent executions, adding observability and abuse detection, and owning platform reliability and performance. The role partners with researchers and product teams to provide safe, usable sandboxing primitives.

Lead and build Thinking Machines’ product policy function, establishing usage, content, safety, model release, and developer-facing policies. Hire and manage a team, create product and model risk review frameworks, develop decision-making infrastructure, and partner with Research, Product, Trust & Safety, Communications, Public Policy, and Legal. Translate complex AI policy questions into actionable executive recommendations and responsible launch mitigations.

Lead Thinking Machines’ global public policy strategy and build its policy team. Set positions on frontier AI legislation, safety, disclosure, open-weight models, export controls, and national security. Represent the company before legislators, regulators, and industry groups; manage counsel and coalition relationships; and establish internal policy infrastructure supporting research, product, safety, legal, and executive decision-making across U.S., EU, and international jurisdictions.

Lead the Fine-tuning Science team and set the research agenda for frontier model customization and post-training techniques. Conduct hands-on research in LoRA, parameter-efficient fine-tuning, reinforcement learning, and training stability. Hire and mentor researchers, improve large-scale training reliability and efficiency, collaborate across research, infrastructure, and product teams, and represent findings through publications and community contributions.

Advance fine-tuning and post-training research for frontier AI models, including LoRA and parameter-efficient fine-tuning. Improve the quality, efficiency, stability, and reliability of large-scale fine-tuning and reinforcement-learning runs. Translate research into Tinker’s training defaults, APIs, and open-source cookbook recipes, while communicating findings through papers, technical posts, and community contributions.

Lead commercial and product legal work for advanced AI research and products. Draft and negotiate enterprise, data licensing, technology transaction, research collaboration, product partnership, safety, and open source agreements. Advise technical and cross-functional teams, support compliance and product counseling, and build legal infrastructure including playbooks, systems, automation, and outside counsel relationships.

Build and operate engineering infrastructure supporting post-training AI research, including reinforcement learning training systems, sandboxed environments, data pipelines, and agent scaffolding. Embed with research teams, lead projects end to end, improve large-scale systems, debug intermittent failures, and communicate complex technical concepts clearly. The role requires strong Python and engineering fundamentals, autonomy, and experience working in fast-moving codebases.

Develop reliable evaluations and research signals for frontier AI models. Responsibilities include creating capability and usability evaluations, improving grader reliability, auditing benchmarks, building agentic evaluation environments and user simulators, and assessing nuanced behaviors such as preferences, personalization, biases, and values. The role collaborates closely with post-training researchers and engineers and may involve Python, deep learning frameworks, distributed training, and novel evaluation methodology research.

Ensure reliability of large-scale GPU supercomputing clusters by diagnosing hardware, firmware, driver, kernel, and operating-system issues. Investigate root causes, automate fleet monitoring, analyze reliability data, manage firmware qualification and rollouts, coordinate directly with hardware vendors, oversee RMAs, and improve GPU health monitoring. The role also requires writing postmortems and vendor cases and owning reliability initiatives across teams.