We are a small, technical team with a clear mission: make rigorous experimentation the default — not the exception — for how people and organizations make decisions.
What we value
We build on solid foundations — not heuristics, not vibes. If a claim can be tested, we test it. If an assumption is hidden, we surface it. The platform is a reflection of how we think.
We stay small deliberately. Every person on the team owns significant surface area. We move fast by staying focused — one platform, built right, rather than ten features built carelessly.
The distance from a paper to a shipped feature should be short. We hire people who can read the literature and implement from it — and who want to see their work running in production.
Open roles
All roles are remote. We work across time zones asynchronously.
Lead the statistical engine behind Reinforce OS. Design and implement estimators for average treatment effects, heterogeneous effects, and mediation. Expected to publish.
Build the adaptive layer of Reinforce OS. Implement bandit algorithms, contextual policies, and online learning loops that turn experiment results into updated interventions in real time.
Own the product surface of Reinforce OS. Work across the Python/FastAPI backend and Next.js frontend to build the interfaces that make causal experimentation accessible to practitioners.
Work directly on the intersection of causal inference and foundation models. Help design experiments, implement estimators, and contribute to research that will ship in the platform.
Don't see your role?
If you care deeply about causal inference, reinforcement learning, or making rigorous science accessible — we want to hear from you, regardless of whether a specific role is listed.
careers@dooperator.ai →