Research Infrastructure Engineer, Research Acceleration
$350k - $475k • San Francisco
Posted 1mo ago
About the job
Thinking Machines Lab is seeking engineers to build the libraries and tools that accelerate research. You will own internal infrastructure, including evaluation libraries, RL training libraries, and experiment tracking platforms, to build systems that compound research velocity over time. This is a collaborative role where 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.
Responsibilities
- 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, including implementing monitoring and observability.
- Partner directly with researchers to identify bottlenecks and unlock new capabilities, owning research tooling like a product manager.
- Collaborate with infrastructure, data, and product teams to integrate tools across the technical stack.
Requirements
- 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 (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.
- 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.
Benefits
- Health, dental, and vision benefits
- Unlimited PTO
- Paid parental leave
- Relocation support