Software Engineer, Research Tools

Thinking Machines Lab•San Francisco, CA

1d•$300,000 - $475,000•Onsite

About The Position

The mission of Thinking Machines is to build AI that extends human will and judgment. We are a team of full stack generalists with strong product instincts who work closely with researchers. We build systems that compound research and engineering velocity over time. We own the internal platform researchers use every day to manage and monitor training runs and evaluations, inspect and debug model trajectories, and compare results on shared leaderboards. You’ll own key parts of this platform, including evaluation and training libraries, experiment-tracking systems, and visualization tools. You’ll identify researchers’ most important bottlenecks and turn them into reliable, generalizable systems. Our team is still small—expect to participate in research meetings, build close relationships with researchers, and gather feedback frequently to develop conviction about where we should invest next. This role requires technical judgment, close cross-functional collaboration, and product intuition. Success means researchers trust your systems to work, rely on them every day, and find them genuinely delightful to use.

Requirements

A bachelor’s degree, or equivalent practical experience, in computer science, engineering, machine learning, or a related field.
Two years of post-grad work experience as a software engineer or ML engineer, exclusive of internships.
Strong software engineering fundamentals and experience building reliable, maintainable systems.
Proficiency in at least one backend programming language; we primarily use Python and Rust. We use React and Typescript on the frontend.
Experience working with databases, data warehouses (Clickhouse), caching systems such as Redis, and other data infra.
Comfort working across the stack and owning projects from initial problem discovery through deployment and operation.
Experience collaborating with cross-functional partners and subject-matter experts.

Nice To Haves

A track record of building tools for researchers, improving developer productivity, or creating technical products for technical users.
Experience building polished, intuitive user-facing applications that demonstrate strong product judgment and attention to detail on UI/UX.
Experience at a startup or on a small team, building technically complex products end to end.
Experience building or maintaining ML research infrastructure, such as training frameworks, evaluation libraries, or experiment-tracking systems.
Experience working closely with researchers to understand and solve their tooling needs.

Responsibilities

Design, build, and maintain research infra, including evaluation frameworks, training systems, experiment tracking platforms, and visualization tools.
Work across backend systems, data pipelines, and user-facing applications to deliver tools end to end.
Partner directly with researchers to identify bottlenecks and unlock new capabilities. Treat research tooling as a product: proactively gather feedback, set priorities, and measure adoption.
Build systems for reproducibility, traceability, and robust quality control across research experiments and model training runs, with monitoring and observability built in.