Thinking Machines Lab
RL Systems - Infra
San Francisco
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About the role
- This role sits at the intersection of research and large-scale systems engineering: a builder who understands both the algorithms behind RL and the realities of distributed training and inference at scale.
- You'll wear many hats, from optimizing rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers and infra teams to make reinforcement learning stable, fast, and production-ready.
- Note: This is an "evergreen role" that we keep open on an on-going basis to express interest.
Responsibilities
- Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems.
- Collaborate with researchers to translate algorithmic ideas into production-grade training pipelines.
- Build evaluation and benchmarking infrastructure that measures model progress on helpfulness, safety, and factuality.
- Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
Requirements
- Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
- Experience training or supporting large-scale language models with tens of billions of parameters or more.
- Familiarity with monitoring and observability tools (Prometheus, Grafana, OpenTelemetry).
Nice to have
- we encourage you to apply if you meet some but not all of these:
Compensation
- Depending on background, skills and experience, the expected annual salary range for this position is $425,000 - $475,000 USD.
Benefits
- Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
- Design, build, and optimize the infrastructure that powers large-scale reinforcement learning and post-training workloads.
- Bachelor's degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
- Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
Company info
- The mission of Thinking Machines is to build AI that extends human will and judgment.
Visa & Work Authorization
- We sponsor visas.
- While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
This listing is sourced directly from Thinking Machines Lab's careers page and normalized into a canonical job model.