Reinforcement Learning Engineer - Ingénieur(e) en apprentissage par renforcement

We are seeking a Reinforcement Learning Engineer with experience manipulating virtual environments to train autonomous agents. This role focuses on the design of robust simulation environments, reward structures, and policy architectures that can navigate complex, multi-sensor landscapes. Key Responsibilities Cross-Functional Coordination: Work with partner ML and Annotation engineers and TPMs to spec out data, simulation, and training requirements. Environment Design: Build and maintain high-fidelity 2D/3D simulation environments (using tools like Unity, Unreal, or Isaac Sim) that serve as the training ground for RL agents. Reward Engineering: Design and tune complex reward functions that align agent behavior with product goals and safety constraints. Algorithm Implementation: Develop and optimize RL algorithms (e.g., PPO, SAC, or Offline RL) capable of handling high-dimensional 3D observation spaces. Sim-to-Real Strategy: Analyze the "reality gap" and implement domain randomization or adaptation techniques to ensure models perform reliably in real-world scenarios.