Research Infrastructure Engineer, Research Acceleration

We’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.

• →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.

Minimum 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.

• 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.