onsite
Reinforcement Learning (RL) Engineer
Reinforcement Learning (RL) Engineer
Genrobotics is seeking a Reinforcement Learning (RL) Engineer to develop and optimize RL algorithms for real-world and simulation applications. The role involves designing, implementing, and deploying RL models, working with simulation environments, and collaborating with cross-functional teams to integrate RL with classical control systems.
About the role
About the Role
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 product or 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.
Skills Required
- Strong expertise in Python, with experience in ML frameworks like PyTorch or TensorFlow.
- Deep understanding of:
- Markov Decision Processes (MDP)
- Policy & value-based RL
- Deep learning architectures (CNN, RNN, Transformers)
- Control theory fundamentals
- Experience with RL libraries (stable-baselines3, RLlib, CleanRL, etc.).
- Experience with simulation tools or robotics middleware (ROS/ROS2, Gazebo).
Added Advantage
- Experience in robotics, mechatronic, or embedded systems.
- Experience with C++ for performance-critical applications.
- Knowledge of GPU acceleration, CUDA, or distributed training.
- Experience bringing RL models from simulation to real-world (Sim2Real).
- Experience with cloud platforms (AWS/GCP/Azure).
Experience
- 2–4 years of hands-on RL experience (academic or industry).
- Published RL research papers (optional but preferred).
Qualifications
- Bachelor’s/Master’s/PhD in Computer Science, Robotics, AI, Machine Learning, or related field