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["Supercomputing","Artificial Intelligence","Computer Hardware","High-Performance Computing"]

Research Software Engineer, Post Training

San Francisco, CAOnsiteFull Time$350,000–$475,000 /yrPosted 1 month ago

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Required skills

  • Python
  • Machine Learning
  • Physics
  • Mathematics
  • Project Leadership
  • large fast-moving codebases
  • debugging systems
  • intermittently
  • scale
  • Clarity in communication

Nice to have

  • sandboxed execution environments
  • containerized execution environments
  • large scale
  • deep learning framework
  • PyTorch
  • TensorFlow

Role summary

Thinking Machines is seeking a Research Software Engineer to join their post-training AI research team. This role involves embedding within a research team to build, harden, and improve the systems and infrastructure necessary for their success. Responsibilities include designing, building, and operating infrastructure such as RL training systems, sandboxing, data pipelines, and agent scaffolding. The ideal candidate will have a strong foundation in Computer Science, Machine Learning, or related fields, proficiency in Python, experience with large codebases, and strong debugging skills. Preferred qualifications include experience with containerized environments and deep learning frameworks.

About Thinking Machines

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.

About the Role

This role is responsible for building and strengthening the engineering foundations that our post-training research teams depend on. You'll embed within a research team, and build or improve the systems required for the team to succeed. Our research teams are small, and the role carries a corresponding degree of autonomy and responsibility.

What You’ll Do

  • Embed within a research team to build, harden, and improve the systems and infrastructure required for the team to succeed.

  • Design, build, and operate infrastructure research teams depend on, including RL training systems, sandboxing, data pipelines, and agent scaffolding.

Skills and Qualifications

Minimum qualifications:

  • Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.

  • Experience leading projects end to end, working in large fast-moving codebases, and writing code others have depended and built on.

  • Strong proficiency in Python and strong engineering fundamentals, with experience debugging systems that fail intermittently and at scale.

  • Clarity in communication, an ability to explain complex technical concepts in writing.

  • Strong autonomous drive to progress towards the team’s goals with an ownership mindset.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but preferably some:

  • Experience building or iterating with sandboxed or containerized execution environments at a large scale.

  • Experience in a role where the team's priorities set yours, and a track record of success in such a role.

  • Familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).

Logistics

  • Location: This role is based in San Francisco, California.

  • Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.

  • Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

  • Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

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