RL Environments Architect

What you'll do

• Build a modular environment framework with APIs, curriculum scaffolds, and configurable reward and termination schemas.
• Define quality standards, coverage metrics, invariance checks, trace audits, and rollout telemetry.
• Work with researchers to turn real-world tasks into simulations, synthetic data generators, and evaluation suites.

What they ask for

• Experience building RL environments or simulators, such as custom physics, multi-agent systems, or tool APIs.
• Ability to design reward functions, scenario taxonomies, and QA pipelines.
• Ability to collaborate across research and engineering to deliver testable environments.

AI focus

Builds reinforcement-learning environments and evaluation systems for training and testing agentic AI, with attention to simulation fidelity, reward integrity, and safety.