Description
The RL Environments Architect will design, instrument, and govern simulated environments for reinforcement-learning agents, including modular frameworks, APIs, reward and termination schemas, telemetry, coverage and invariance checks, and synthetic data generators. The role partners with researchers to translate real-world tasks into robust simulations and establishes standards for data quality, reproducibility, and failure-surface coverage across large-scale agent evaluation systems.
