Machine Learning Engineers: Scenario Building for Reinforcement Learning
Terac · 100% remote · Contract · Posted
- Pay
- $90/hr
- Location
- Worldwide
- Languages
- English
- Hours
- Flexible
- Openings
- Not listed
- Level
- Experienced
Summary
Design and construct scenarios for reinforcement learning agents on a remote platform, configure environmental parameters, run test interactions, document workflow, and provide feedback in an interview.
What you'll do
- Design and construct scenarios for reinforcement learning agents
- Configure environmental parameters
- Define spatial constraints
- Run preliminary agent interactions to test setups
- Document workflow and note friction points
- Participate in an interview to share platform usability feedback
Requirements
- Hands-on experience in simulation design and reinforcement learning environments
- Comfortable configuring complex platform interfaces
- Ability to define structured agent scenarios
- Familiarity with RL frameworks
Skills
- Reinforcement Learning
- Simulation Design
- RL Frameworks
- Scenario Building
- Platform Interfaces
Full description
What We're Researching
We're hiring AI researchers and machine learning engineers to participate in building worlds within a reinforcement learning platform. This work directly influences how agents interact with complex, simulated environments during their training cycles. Your technical expertise will help us refine the tools and interfaces used to create robust testing scenarios.
How It Works
You will connect to our remote platform to design and construct specific scenarios for reinforcement learning agents. Throughout the session, you will configure environmental parameters, define spatial constraints, and run preliminary agent interactions to test your setup. You will document your workflow and note any friction points encountered while structuring the environment. Finally, you will participate in an interview to share your feedback on the platform's overall usability.
Who This Is For
This study targets professionals with hands-on experience in simulation design and reinforcement learning environments. We welcome machine learning engineers, AI researchers, simulation developers, and technical game designers accustomed to RL frameworks. Candidates should be highly comfortable configuring complex platform interfaces and defining structured agent scenarios.
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