Startups · AI

Software Engineer, Research Tools

Thinking Machines · San Francisco · On-site

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About Thinking Machines

Connectionism: Research Blog by Thinking Machines Lab. Backed by a16z, Accel and GV.

About the role

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.

What they're looking for

  • 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
More about this role

The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.

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,...

Read the full posting on Thinking Machines's site ↗

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