RL Environment Engineers: Building Tool Gyms For Knowledge Work

Role Summary

What We're Researching

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.

How It Works

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.

Who This Is For

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.

Ready to participate?

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About Terac

Terac is building the world's largest pool of vetted human experts for AI. Researchers, AI labs, and product teams use Terac to recruit, screen, and pay study participants across industries, languages, and skill sets.

Learn more at or on YouTube at @jointerac.

Responsibilities

• 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.
• Who Should Apply
• 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