Research, Coding Agents

Bison; San Francisco (CA); On-site; USD 400,000 - 500,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines in San Francisco seeks a researcher for the Coding Agents team to advance agentic coding through RL, data pipelines, and model post-training infrastructure. This is a hands-on research role with ownership over recipes, data, and evaluation.

You will design RL training jobs, build sandboxed coding environments, generate synthetic data, and collaborate with infra and evals to ship results in model releases.

Qualifications

• Strong engineering skills and ability to contribute code in complex codebases.
• Proficiency in Python and at least one deep learning framework (PyTorch, TensorFlow or JAX).
• Bachelor’s degree or equivalent in CS/ML/Physics/Math with strong grounding.
• Clear writing and communication of technical concepts.

Responsibilities

• Design and run RL training jobs targeting agentic coding capabilities, iterating on recipes and data.
• Build and improve sandboxed coding environments and reward signals used for training and evaluation.
• Generate and curate synthetic coding data and build scalable data pipelines.
• Design evaluations that measure real-world coding usefulness and train models against them.
• Debug and analyze large RL runs to identify confounders and failure modes.
• Collaborate with infra, evals, and post-training teams to ship results into model releases.

Skills

Strong engineering

Python

Deep learning frameworks

Education

Bachelor's degree or equivalent (CS/ML/Physics/Math)

PhD in CS/ML/Physics/Math (optional)

Job description

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.

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

Preferred qualifications

• Experience building synthetic data pipelines and systems that were adopted by others on your team and remain in use today.
• Experience owning the end-to-end cycle of identifying gaps in model usability and closing them through custom evaluations and training data.
• Experience making large-scale agentic RL infrastructure reliable given the long tail of failures that surface at scale.
• Experience improving the coding capabilities of a frontier model.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

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 $400,000-$500,000 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.