Research, Coding Agents

About the role

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

- The Coding Agents team makes our models world-class at agentic coding — writing, debugging, and reasoning about code across long-horizon, multi-turn tasks.

- You'll join a small, high-leverage team responsible for the recipes, data, and infrastructure behind coding capability gains in every model release.

- The team owns the full coding post-training stack: synthetic and human data generation, RL environments and sandboxes, reward and grading design, and large-scale training runs.

- This is a research role with real ownership — you'll shape technical direction, not just execute against a spec.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

WHAT YOU’LL DO

- Design and run RL training jobs targeting agentic coding capabilities, iterating on recipes and data.

- Build and improve the sandboxed coding environments and reward signals that models are trained and evaluated against.

- Generate and curate high-quality synthetic coding data, and build scalable, general-purpose data pipelines.

- Design evals that measure real-world coding usefulness, and train models against them to deliver concrete improvements in day-to-day usability.

- Debug and analyze large RL runs to catch confounders, reward hacking, and other RL failure modes.

- Collaborate closely with infra, evals, and other post-training teams on shared data, joint training runs, and usability improvements — and ship the results into model releases.

SKILLS AND QUALIFICATIONS

Minimum qualifications:

- Strong engineering skills, ability to contribute code and debug in complex codebases.

- Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.

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