Software Engineer, Research Tools

Thinking Machines Lab•San Francisco, CA

2d•Onsite

About The Position

Thinking Machines is hiring a full-stack generalist with strong product instincts to build and maintain the internal platform used by researchers to manage and monitor training runs and evaluations, inspect and debug model trajectories, and compare results on shared leaderboards. The role owns key parts of the platform, including evaluation and training libraries, experiment-tracking systems, and visualization tools. The engineer will work closely with researchers to identify important bottlenecks and turn them into reliable, generalizable systems. The team is small, so the role involves participating in research meetings, building close relationships with researchers, and frequently gathering feedback to guide investment priorities. Success means researchers trust the systems, rely on them in their daily work, and find them delightful to use. The position is based in San Francisco, California, or New York, New York. The expected annual salary range is $300,000–$350,000 USD, depending on background, skills, and experience. Visa sponsorship is offered, with no guarantee of success for every candidate or role.

Requirements

A bachelor’s degree or equivalent practical experience in computer science, engineering, machine learning, or a related field.
Two years of post-graduate work experience as a software engineer or ML engineer, excluding internships.
Strong software engineering fundamentals and experience building reliable, maintainable systems.
Proficiency in at least one backend programming language; the team primarily uses Python and Rust.
Experience working with databases, data warehouses such as ClickHouse, caching systems such as Redis, and other data infrastructure.
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 in 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 infrastructure, 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 by proactively gathering feedback, setting priorities, and measuring adoption.
Build systems for reproducibility, traceability, and robust quality control across research experiments and model training runs, with monitoring and observability built in.