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About Traverse

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.

About the Role

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.

In this role, you will

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

About Traverse

About the Role

As a Research Intern, you will work alongside the research team to design and evaluate RL environments for domains where expertise is hard to formalize. You'll get hands-on experience at the frontier of AI training, contributing to projects that ship directly to partner labs. This is a high-impact internship for someone who learns fast, thinks carefully, and wants to do work that matters. We prefer individuals with prior ML or AI experience, however we care more about intellectual curiosity and the ability to reason rigorously about hard problems.

In this role, you will

Assist in designing and evaluating RL environments across new domains
Run experiments to test hypotheses about reward modeling and environment quality
Collaborate with domain experts to understand what mastery looks like in a given field
Contribute to internal research documents and help refine Traverse's methodology
Work directly with research scientists and engineers on active projects

Your background looks something like this

Currently pursuing or recently complet","datePosted":"2026-02-24","employmentType":"INTERN","hiringOrganization":{"@type":"Organization","name":"Traverse","sameAs":"https://www.traverse.so"},"url":"https://jobs.ashbyhq.com/traverse/7129fad1-7ddb-4ee5-a9ba-cb67748096c8","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}}},{"@context":"https://schema.org","@type":"JobPosting","title":"Head of Research","description":"
About Traverse
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.
About the Role
As Head of Research, you will own the entire research function at Traverse, from setting the agenda and publishing papers to building benchmarks and leading the team that figures out how to train AI on the hardest problems in the world.
The core challenge is that most of the economy runs on work where \"good\" is subjective, feedback is delayed, and expertise takes decades to develop. Medicine, law, negotiation, engineering: none of these domains have neat reward signals, and figuring out how to encode what mastery looks like in these fields and turn that into RL environments that actually make models better is an open research problem. You'll be the person solving it.
You'll work directly with the frontier labs we partner with, understand their post-training pipelines, and co-design environments that reflect real hypotheses about how intelligence develops in a given domain. You'll publish novel work that establishes Traverse as a research institution, not just a data vendor.
We're looking for someone with serious research depth in ML and a track record of publishing at top venues, but we care just as much about your ability to reason about hard problems outside of ML. The best candidate probably has both: deep technical chops and genuine curiosity about domains they've never worked in.
In this role, ","datePosted":"2026-02-24","employmentType":"FULL_TIME","hiringOrganization":{"@type":"Organization","name":"Traverse","sameAs":"https://www.traverse.so"},"url":"https://jobs.ashbyhq.com/traverse/55453dae-4d5a-4189-b449-9b591ae1e958","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}}},{"@context":"https://schema.org","@type":"JobPosting","title":"Founding Engineer","description":"
About Traverse
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.
About the Role
As a Founding Engineer, you will build the core infrastructure and environments that train AI models to do tasteful work. You'll operate across the full stack, from designing reward functions and evaluation pipelines to building the tooling that lets us scale environment quality across new domains without it falling apart. This is not a narrowly scoped role. You'll work on whatever matters most, and what matters most changes as we move into new domains and take on new lab partnerships. We care about raw engineering ability and taste, not credentials or keywords on a resume. No prior ML or AI experience is required.
In this role, you will
Design and build RL training environments across multiple verticals, working closely with domain experts and AI Researchers
Build infrastructure and tooling that enables environment quality to scale across domains
Work directly with frontier AI labs to understand their training pipelines and integrate our environments into their workflows
Own problems end-to-end, from scoping through implementation through evaluation
Shape the technical direction of the company as an early team member
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Traverse

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