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Position Overview

We are looking for a Robotics Reinforcement Learning Engineer to develop and deploy reinforcement learning-based locomotion policies for autonomous quadruped robots.

In this role, you will train robotic systems to maintain dynamic balance, respond to physical disturbances, and navigate different terrain conditions. You will use physics-based simulation and reinforcement learning to develop robust control policies, then transfer and validate those policies on physical robotic hardware.

The role combines reinforcement learning, legged robot locomotion, simulation engineering, and real-time deployment, with a strong emphasis on improving reliability when moving from simulation to real-world operation.

Key Responsibilities

• Develop reinforcement learning-based locomotion policies using NVIDIA Isaac Lab, Isaac Gym, RaiSim, or comparable simulation platforms.

• Train parallelized locomotion policies across randomized terrain, friction conditions, and dynamic disturbances.

• Develop blind and vision-assisted locomotion approaches for quadruped robots operating in different environments.

• Implement domain randomization across physical parameters, including friction, motor latency, payload variations, and mass distribution.

• Improve Sim-to-Real transfer to ensure learned policies remain stable and effective when deployed on physical robots.

• Implement and optimize reinforcement learning algorithms, including PPO, using Python, PyTorch, and related tools.

• Deploy trained policies using C++, ONNX Runtime, LibTorch, or other suitable inference frameworks.

• Optimize neural policy inference for high-frequency robotic control and low-latency execution.

• Integrate foot-contact estimation, slip detection, and reflex mechanisms to improve stability and reduce the risk of joint damage.

• Test and evaluate locomotion performance in simulation and on physical quadruped platforms.

• Analyze control failures, investigate performance gaps between simulation and hardware, and implement iterative improvements.

• Work closely with robotics controls, simulation, and embedded systems engineers to support integrated robot autonomy.

Qualifications

• Bachelor's or Master's degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Control Systems, or a related field.

• Approximately 3–5 years of relevant experience in reinforcement learning, robot learning, locomotion control, or autonomous robotics.

• Strong Python programming skills and practical experience with PyTorch.

• Experience training reinforcement learning policies, particularly using PPO or similar algorithms.

• Hands-on experience with NVIDIA Isaac Lab, Isaac Gym, RaiSim, MuJoCo, or other physics-based robotics simulators.

• Understanding of legged robot dynamics, locomotion control, dynamic balance, or contact-rich robotic systems.

• Practical knowledge of domain randomization and Sim-to-Real transfer techniques.

• Experience with C++ and deploying learned models on robotic hardware or edge platforms.

• Familiarity with ONNX Runtime, LibTorch, or comparable inference frameworks is an advantage.

• Knowledge of contact estimation, slip handling, motor dynamics, or real-time control is a plus.

• Strong problem-solving skills and the ability to validate reinforcement learning policies through systematic testing and experimentation.

Why Join?

• Work on advanced robotic autonomy and reinforcement learning technologies.

• Develop locomotion policies that enable quadruped robots to handle complex and unpredictable terrain.

• Gain hands-on exposure to simulation-to-hardware deployment and real-world robotics challenges.

• Collaborate with specialists across robot learning, motion control, and embedded systems.

• Contribute to practical R&D projects focused on improving robotic stability, adaptability, and autonomy.

Work Location & Arrangement

On-site at the robotics R&D facility in New Delhi, India