About the RoleThis role is responsible for advancing the agentic capabilities of our models, with ownership spanning the full development cycle. Our research team is small, and the role carries a corresponding degree of autonomy and responsibility.
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- Build a synthetic data framework used across the team, and create new task environments to scale training across agentic capabilities such as tool use, long-horizon tasks, and complex workflows.
- Address identified gaps through data and recipe work, validating proposed changes through controlled ablations.
- Enhance usability for agent capabilities end to end, translating internal and external feedback into model improvements.
Skills and QualificationsMinimum qualifications:- Strong engineering skills, ability to contribute code and debug in complex codebases.
- Ability to design, run, and interpret experiments with scientific rigor and clarity.
- 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 - we encourage you to apply even if you don't meet all preferred qualifications, but at least some:
- 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 agentic 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 $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.
