Research Engineer, Applied AI

We're hiring a Research Engineer to join our Applied AI team in San Francisco. You'll design and build reinforcement learning environments with verifiable rewards for specific real-world use cases: the simulators, reward functions, and evaluation harnesses that let us measure and improve how models perform on real work.

The work is equal parts ML research and systems engineering. You'll work directly with the founders and our research advisors, taking a use case from problem definition to a reproducible environment that models can be evaluated and trained against.

Design and build RL environments for specific real-world use cases: realistic simulators, tool interfaces, and episodic task generation with proper isolation and reproducibility

Design verifiable reward functions that score correct intermediate actions as well as end states, and hold up against reward hacking

Build and maintain evaluation harnesses that run task suites across frontier and open-weight models, with clean scoring and cost tracking

Run post-training experiments (e.g. RLVR-style fine-tuning of open models) to validate that your environments produce a learnable signal

Package environments and results for reproducibility, and contribute to research write-ups and published evaluations

Help define which use cases we pursue next, informed by where models are weakest

Master's or PhD in AI, Machine Learning, Computer Science, or a closely related field

Solid grounding in reinforcement learning and LLM post-training (reward design, policy optimization, evaluation methodology)

Strong software engineering skills in Python, with code that others can run and build on