Reinforcement Learning (RL) Engineer (2 - 4 Years)

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.

Skills Required : 

• Strong expertise in Python, with experience in MLframeworks 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

Location : Technopark, Thiruvananthapuram