Machine Learning Engineers: Scenario Building for Reinforcement Learning

What you'll do

You connect to Terac's remote platform and build scenarios for reinforcement learning agents. That means configuring environment parameters, defining spatial constraints and running preliminary agent interactions to check that your setup behaves. You write down your workflow and any friction you hit while structuring the environment, and the session ends with an interview about how usable the platform is.

The framing matters: you are both a builder and a tester. The ad describes the work as feeding back into the tools and interfaces used to create testing scenarios, so your notes on what was awkward are as much the product as the scenarios themselves.

Who fits

• Hands-on experience in simulation design and RL environments
• Machine learning engineers, AI researchers, simulation developers and technical game designers who already work with RL frameworks
• Comfort configuring complex platform interfaces and defining structured agent scenarios

The ad states no degree or years-of-experience bar, and no location requirement.

What it pays

Terac lists $90 per hour. The ad does not say how many hours a session lasts, so the total fee depends on the study scope shown once you are matched.

Worth knowing

Good:

• A stated hourly rate well above Terac's simpler tasks
• Work that uses real RL and simulation skills
• Remote, with no location restriction

Less good:

• It reads as a single study, not an ongoing contract
• Session length and number of openings are not published
• Registering with Terac does not guarantee a match

About this listing

The Apply button opens this study on Terac; you sign in or create a Terac account there to apply. Terac lists it as open until 2 November 2026. See Terac.