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

Genrobotic Innovations; Thiruvananthapuram; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Genrobotic Innovations seeks an RL Engineer to design and optimize reinforcement learning systems for real-world and simulation-based applications. You will work on PPO, SAC, TD3, DQN, A3C, TRPO, and more, crafting reward structures and learning workflows for robotics projects.

Join a team that builds simulation environments with PyBullet, Mujoco, IsaacGym, CARLA, Gazebo, or custom environments, and deploys RL models to robots and embedded hardware while collaborating with control engineers and

Qualifications

• Bachelor's/Master's/PhD in Computer Science, Robotics, AI, or related field.
• 2–4 years hands-on RL experience (academic or industry).
• Publications in RL are a plus.

Responsibilities

• Design, implement, and optimize RL algorithms (e.g., PPO, SAC, TD3, DQN, A3C, TRPO).
• Develop custom reward functions, policy architectures, and learning workflows.
• Research state-of-the-art RL techniques and integrate into pipelines.

Skills

Python

PyTorch

TensorFlow

MDP

Policy & value-based RL

Deep learning

Control theory

Education

BSc/MSc/PhD in CS/Robotics/AI

Tools

stable-baselines3

RLlib

ROS/ROS2

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 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