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What We're Researching:
- Building tool-based environments for knowledge work applications.
- Creating robust simulation frameworks where models can learn to interact with complex software tools.
- Supporting advanced reinforcement learning research and training pipelines.
How It Works:
- Spend approximately 20 hours per week developing and testing new tool environments.
- Write code to simulate knowledge work tasks and ensure realistic and stable agent interactions.
- Participate in remote video check-ins to discuss architecture decisions and troubleshoot implementation blockers.
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.
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.
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