Reinforcement Learning (RL) Engineer

5 years

0 Lacs

Posted:4 days ago| Platform: Linkedin logo

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Job Type

Full Time

Job Description

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

  • 2–5 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

Skills: deep learning,c++,robotics,reinforcement,cnn,rnn

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