Jobs at Thinking Machines Lab

Own full-cycle recruiting for go-to-market roles, including account executives, sales leaders, solutions engineers, partnerships, customer success, and revenue operations. Partner with GTM leadership on role definition, sourcing, assessment, interview design, candidate closing, and hiring processes. Build talent pipelines, evaluate sales capabilities, deliver strong candidate experiences, track recruiting metrics, and improve recruiting tools, workflows, compensation, and leveling guidance.

Build and operate distributed production inference systems for large-scale AI models. Responsibilities include request routing, load balancing, batching, multi-node serving, GPU utilization, latency and throughput optimization, observability, inference engine development, debugging, and production on-call support. Collaborate with research and infrastructure teams to deploy evolving model architectures reliably and efficiently.

Design and optimize infrastructure for large-scale reinforcement learning and post-training workloads. Improve distributed training reliability, scalability, throughput, observability, and reproducibility. Build evaluation and benchmarking systems for model helpfulness, safety, and factuality, while collaborating with researchers to productionize algorithms and sharing technical learnings through documentation, open-source work, or reports.

Own compute allocation for shared supercomputing resources, balancing research demand, workload requirements, hardware suitability, capacity, timing, and dependencies. Partner with researchers and infrastructure engineers to improve scheduling, placement, utilization, and capacity planning. Teach teams to estimate compute needs, explain allocation tradeoffs, develop guidance and tools, and improve decisions through usage and performance analysis.

Operate and scale production inference systems serving live, multi-tenant traffic. Own model rollouts, observability, capacity planning, incident response, reliability, failover, and cost optimization. Partner with research and inference teams to productionize serving techniques while maintaining system performance and resilience.

Design, build, and operate distributed infrastructure for large-scale AI model training and inference. Responsibilities include orchestration, scheduling, storage, resource management, reliability, observability, distributed failure debugging, and developing internal Python and Go libraries and APIs. The role partners with researchers and platform engineers and supports infrastructure operating across thousands of accelerators.

Support 2–3 executives across legal, operations, and communications by managing calendars, meetings, travel, communications, action items, recruiting coordination, company events, and leadership projects. The role requires autonomy, discretion, adaptability, and proactive support in a fast-moving AI startup environment. The position is based in San Francisco and includes coordination across teams and time zones.

Build developer-facing tools, integrations, documentation, demos, and machine learning recipes for Inkling and Tinker. Collaborate directly with open source developers and researchers to debug issues, gather feedback, improve products, and shape the developer experience roadmap. Represent the company through technical writing, talks, tutorials, livestreams, conferences, and meetups. Requires strong Python engineering skills and hands-on experience fine-tuning, evaluating, or deploying large language models.

Build and improve Tinker’s developer experience by creating cookbook recipes, documentation, tools, integrations, demos, and library features. Work directly with users to debug fine-tuning pipelines, optimize workflows, identify product improvements, and influence the platform roadmap. The role also involves sharing technical learnings through blog posts, guides, talks, and code examples while collaborating with research and infrastructure teams.

Own onboarding and early employee lifecycle operations, employment compliance, immigration case management, HRIS and payroll systems, benefits administration, people programs, documentation, and cross-functional support. Build scalable infrastructure, playbooks, dashboards, and processes for a rapidly growing AI company while exercising discretion with sensitive employee information and making sound decisions in an ambiguous environment.

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.

Build reinforcement learning and post-training systems for Tinker, including algorithms, data pipelines, numerics, kernels, and distributed training infrastructure. Collaborate with researchers, companies, and external partners; debug real-world RL runs, optimize training pipelines, and enable frontier-model customization. The role requires Python and deep learning framework expertise, with preferred experience in RL stability, low-precision training, LLM serving, scaling studies, and open-source frameworks.

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.