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

Genrobotics; Thiruvananthapuram; On-site; INR 900,000 - 1,800,000; Full time

Job summary

Genrobotics is seeking a highly skilled Reinforcement Learning Engineer to design, implement, and optimize RL algorithms for real‑world and simulation environments. The role emphasizes production deployment on robots and embedded systems with strong ML foundations and control theory expertise.

Responsibilities include building RL policies, custom reward functions, and managing large‑scale experiments while collaborating across robotics, perception, and software teams.

Qualifications

• Bachelor’s/Master’s/PhD in Computer Science, Robotics, AI, or related field.
• 2–4 years of hands‑on RL experience (academic or industry).
• Experience deploying RL models in production on real or embedded systems.

Responsibilities

• Design, implement, and optimize RL algorithms (PPO, SAC, TD3, DQN, A3C, TRPO).
• Develop custom reward functions, policy architectures, and learning workflows.
• Create and integrate simulation environments (PyBullet, Mujoco, Gazebo, CARLA) and real hardware interfaces.
• Deploy RL models on robots, embedded hardware, or autonomous platforms.
• Collaborate with robotics, perception, simulation, and software teams; document results clearly.

Skills

Python

PyTorch/TensorFlow

MDP & RL

CNN/RNN/Transformers

Control theory

RL libraries

ROS/Gazebo

Education

Bachelor/Master/PhD in CS/Robotics/AI

Tools

stable-baselines3

RLlib

CleanRL

ROS

Gazebo

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 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.

Location

Technopark, Thiruvananthapuram

Skills: rnn,c++,robotics,pytorch,cnn,deep learning,tensorflow.,reinforcement