Research Scientist

Traverse is a research data lab building reinforcement learning environments for frontier AI labs. We focus on the non-deterministic, taste-dependent work that makes up most of the economy and that nobody else has figured out how to train models on. We work directly with the labs building the most capable models on earth as a thought partner. Backed by Y Combinator.

As a Research Scientist, you will design and build RL environments that teach models to do work that has historically required years of human expertise. You'll work at the boundary of research and domain knowledge, figuring out how to formalize what good performance looks like in messy, real-world domains and turning that into training signal that actually works.

We're looking for people who think carefully about what makes a good environment, not just what makes a functional one. No prior ML or AI experience is required - we care about the ability to reason rigorously about hard problems and learn fast.

• Research and develop novel approaches to reward modeling, environment design, and evaluation for non-deterministic domains
• Collaborate with domain experts to understand what mastery looks like in a given field and translate that into training signal
• Build and run experiments to validate that environments actually improve model capabilities
• Contribute to Traverse's research output and help establish our methodology across new verticals
• Work directly with partner labs to integrate environments into their post-training pipelines

• MS, PhD, or equivalent depth of experience in any rigorous field
• Strong implementation skills and ability to run experiments quickly
• Genuine curiosity about domains outside your own and how expertise works in them
• You go deep on problems and don't stop at the obvious answer

• Experience with reinforcement learning, RLHF, reward modeling, or LLM evaluation