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We're hiring an RL environment engineer to help build tool-based environments, commonly known as tool gyms, for knowledge work applications. This paid engagement focuses on creating robust simulation frameworks where models can learn to interact with complex software tools. The resulting environments will directly support advanced reinforcement learning research and training pipelines.
You will spend approximately 20 hours per week developing and testing new tool environments. This involves writing code to simulate various knowledge work tasks, ensuring realistic and stable agent interactions. You will also participate in remote video check-ins to discuss architecture decisions and troubleshoot implementation blockers. Throughout the project, you will iterate on environment designs based on model performance and technical feedback.
We are looking for specialized machine learning professionals with hands-on experience in reinforcement learning environments. We welcome RL engineers, AI researchers, simulation developers, and machine learning infrastructure specialists. Ideal candidates have previously built or maintained custom gyms and are comfortable committing to a part-time weekly schedule.
Design and implement tool-based environments for knowledge work simulations.
Write clean and modular code to support reinforcement learning training pipelines.
Troubleshoot and refine environment mechanics based on testing feedback.
Collaborate asynchronously and participate in remote progress check-ins.
Professional experience as a machine learning or reinforcement learning engineer
Hands-on background building custom RL environments or tool gyms
Ability to commit to approximately 20 hours of work per week
Comfortable discussing technical architecture and implementation strategies
$120 per hour
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