About the RoleWe're looking for engineers to build the libraries and tools that accelerate research at Thinking Machines. You'll own internal infrastructure - evaluation libraries, RL training libraries, experiment tracking platforms - and build systems that compound research velocity over time.
This is a collaborative role. You will work directly with researchers to identify bottlenecks and pain points. Success means researchers trust your systems to just work and find them a delight to use.
What You'll Do- Design, build, and operate research infrastructure including evaluation frameworks, RL training systems, experiment tracking platforms, visualization tools, and shared utilities.
- Develop high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessment.
- Build systems for reproducibility, traceability, and robust quality control across research experiments and model training runs. Implement monitoring and observability.
- Partner directly with researchers to identify bottlenecks and unlock new capabilities. Own research tooling like a product manager, proactively seeking feedback and tracking adoption.
- Collaborate with infrastructure, data, and product teams to integrate tools across the technical stack.
Skills and QualificationsMinimum qualifications:
- Bachelor's degree or equivalent experience in computer science, engineering, machine learning, or similar.
- Strong software engineering fundamentals with a track record of building reliable, maintainable systems.
- Proficiency in at least one backend language (we use Python or Rust).
- Comfort operating across the stack and owning projects end-to-end.
- Experience in highly collaborative environments involving many different cross-functional partners and subject matter experts.
Preferred qualifications - we encourage you to apply if you meet some but not all of these:
- Track record building tooling for researchers that achieved high adoption without top down mandates.
- Experience building or maintaining ML research infrastructure such as training frameworks, evaluation libraries, or experiment tracking systems.
- Contributions to open-source ML tools or widely-used internal frameworks at research-focused organizations.
- Record of publications or technical writing on ML systems, infrastructure, or tooling.
- Background working closely with ML researchers to understand and solve their tooling needs.
- Familiarity with distributed systems, modern ML frameworks (PyTorch, JAX), and data processing at scale.
- Experience with research observability tools, distributed compute frameworks (Ray, Spark), or large-scale evaluation pipelines.
Logistics- Location: This role is based in San Francisco, California or New York, NY.
- Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
- Visa sponsorship: 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.
- Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
