Member of Technical Staff

TraverseMember of Technical StaffOverviewApplicationLocationSan FranciscoEmployment TypeFull timeLocation TypeOn-siteDepartmentEngineeringAbout TraverseTraverse 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 RoleAs a Member of Technical Staff, you will build the core infrastructure and environments that train AI models to do real 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 willDesign and build RL training environments across multiple verticals, working closely with domain experts and ML engineersBuild infrastructure and tooling that enables environment quality to scale across domainsWork directly with frontier AI labs to understand their training pipelines and integrate our environments into their workflowsOwn problems end-to-end, from scoping through implementation through evaluationShape the technical direction of the company as an early team memberYour background looks something like thisExceptional software engineering fundamentals and ability to ship production-quality code quicklyComfort with ambiguity and a bias toward building rather than planningTrack record of owning complex technical projects end-to-endYou learn fast and go deep on whatever you're working onBonusFamiliarity with ML training pipelines, RL concepts, or LLM post-trainingExperience building evaluation frameworks, reward models, or RL environmentsExperience at an early-stage startupCompensationNegotiable - Offers Equity