We are seeking a highly skilled Reinforcement Learning (RL) Engineer to develop, implement, and optimize RL algorithms for real-world and simulation-based applications. The ideal candidate has strong foundations in machine learning, deep learning, control systems, and hands-on experience deploying RL models in production or embedded systems.
Responsibilities
- Design, implement, and optimize RL algorithms such as PPO, SAC, TD3, DQN,A3C, TRPO, etc.
- Develop custom reward functions, policy architectures, and learning workflows.
- Conduct research on state-of-the-art RL techniques and integrate into productor research pipelines.
- Build or work with simulation environments such as PyBullet, Mujoco, IsaacGym, CARLA, Gazebo, or custom environments.
- Integrate RL agents with environment APIs, physics engines, and sensor models.
- Deploy RL models on real systems (e.g., robots, embedded hardware, autonomous platforms).
- Optimize RL policies for latency, robustness, and real-world constraints.
- Work with control engineers to integrate RL with classical controllers (PID, MPC, etc.)
- Run large-scale experiments, hyper parameter tuning, and ablation studies.
- Analyse model performance, failure cases, and implement improvements.
- Work closely with robotics, perception, simulation, and software engineering teams.
- Document algorithms, experiments, and results for internal and external stakeholders.