RL Environment Engineers: Building Tool Gyms For Knowledge Work

Visa Hunt; United States; On-site; USD 152,000 - 179,000; Part time

Job summary

Terac is seeking an RL environment engineer to develop tool-based environments for knowledge work simulations and reinforcement learning training pipelines. You will write modular code and refine environment mechanics while collaborating remotely on architecture decisions and progress check-ins.

The role targets specialized ML professionals with hands-on RL experience and the ability to commit to roughly 20 hours per week. Compensation is $120 per hour.

Qualifications

• Specialized ML professionals with hands‑on experience in reinforcement learning environments.
• Potential candidates include RL engineers, AI researchers, simulation developers, and ML infrastructure specialists.
• Able to commit to approximately 20 hours per week.

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.

Skills

Reinforcement learning

Simulation development

Remote collaboration

Tools

Custom RL environments

Job description

What We're Researching

We are 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.

What You'll Do

• 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.

Compensation

$120 per hour

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.