Machine Learning Engineers: Scenario Building Reinforcement Learning

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.

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.

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.

• Design and build specific scenarios within a remote reinforcement learning platform
• Configure environmental parameters and define agent interaction rules
• Test initial agent behaviors to validate your scenario structure
• Walk us through your workflow and highlight areas for platform improvement

• Professional experience in machine learning or artificial intelligence research
• Hands-on background in building simulations or reinforcement learning environments
• Familiarity with configuring platform interfaces and defining reward structures
• Comfortable articulating technical feedback during a remote interview

$90 per hour

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.

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