Thinking Machines Lab
Software Engineer, Research Tools
$300K–$350K
San Francisco +1 · On-site · Full-time · Posted Aug 31, 2026
- Location
- San Francisco
New York - Experience
- Two years of post-grad work experience as a software engineer or ML engineer, exclusive of internships.
- Education
- Bachelor’s degree, or equivalent practical experience, in computer science, engineering, machine learning, or a related field.
- Visa sponsorship
- Visa sponsorship is offered; success is not guaranteed for every candidate or role.
About the job
Build and maintain internal research tools that help researchers manage training and evaluation runs, inspect model behavior, and compare results.
What you'll do
- Design and maintain evaluation frameworks, training systems, experiment-tracking platforms, and visualization tools.
- Deliver end-to-end tools across backend systems, data pipelines, and user-facing applications.
- Partner with researchers to identify bottlenecks and build reliable systems for reproducibility, traceability, quality control, monitoring, and observability.
What they ask for
- 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.
- Experience with databases, data warehouses, caching systems, and other data infrastructure; comfort owning projects across the stack.
Nice to have
- Experience building tools for researchers, improving developer productivity, or creating technical products for technical users.
- Experience building or maintaining ML research infrastructure, such as training frameworks, evaluation libraries, or experiment-tracking systems.
Pay
$300,000 - $350,000 USD annual salary range (base salary)
Tools
Python, Rust, React, Typescript, Clickhouse, Redis
AI focus
Build research infrastructure for model training and evaluation, including tools to inspect and debug model trajectories and compare results.
Model-written summary · the original posting is authoritative