Jobs at Thinking Machines Lab

Develop and evolve Thinking Machines’ visual brand across websites, launches, publications, and physical objects. Design, build, and improve digital experiences using code, while creating reusable tools, artwork generators, data-driven charts, and publishing components. Collaborate with illustrators, animators, graphic designers, engineers, and researchers to turn concepts into polished, accessible work. Contribute original ideas, manage open-ended projects, incorporate feedback, and explore agentic workflows and emerging technology.

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.

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.

Own the reliability, performance, and uptime of large-scale post-training and reinforcement learning systems. Debug distributed failures across accelerators, networking, storage, schedulers, and training frameworks; build monitoring, alerting, recovery, checkpointing, and scheduling tools; improve cluster utilization and fault tolerance; support production model runs through on-call rotations and postmortems; and partner closely with research teams during active training runs.

Owns end-to-end reliability for Tinker, including CI/CD, observability, service-level objectives, incident response, multi-tenant isolation, resource scheduling, and vulnerability remediation. The role designs monitoring for distributed training systems, improves recovery and checkpointing, and operates Kubernetes clusters supporting heterogeneous GPU workloads. It collaborates closely with platform, security, engineering, and research teams to improve production resilience and utilization.

Build and scale infrastructure supporting frontier AI model training, research, and product development. Responsibilities may include operating Kubernetes clusters with GPU workloads, developing data pipelines with Spark, improving developer productivity tooling, debugging across application, operating system, and network layers, and delivering reliable distributed systems end to end.

Build and ship AI-powered products from prototype to production. Develop full-stack web features, including backend services, APIs, and polished frontend interfaces. Collaborate with researchers, product managers, and designers to translate model capabilities into usable experiences. Own technical architecture, scalability, reliability, and maintainability while making pragmatic tradeoffs in ambiguous environments. Help establish engineering practices and standards for a growing product engineering team.

Build and maintain research infrastructure, including evaluation frameworks, training systems, experiment tracking, data pipelines, and visualization tools. Partner directly with researchers to identify bottlenecks, improve productivity, and deliver reliable end-to-end systems. Develop reproducibility, traceability, quality control, monitoring, and observability capabilities while treating research tooling as a product.