AI Research Engineer - (Immediate Joiners - Navi Mumbai)

Job Description

Position Overview
We are looking for a passionate and skilled AI Research Engineer to join our team and advance the field of autonomous systems. In this role, you will focus on developing cutting-edge algorithms in Reinforcement Learning (RL), Imitation Learning, and Autonomous Decision-Making to enable robots to learn, adapt, and make decisions in complex, dynamic environments. You will work alongside other AI researchers and engineers to push the boundaries of autonomous decision-making in real-world robotics applications.

Key Responsibilities

• Conduct research and development in Reinforcement Learning (RL) and Imitation Learning to enable robots to learn complex tasks through both trial-and-error and expert demonstrations.
• Design and implement novel algorithms for autonomous decision-making, optimizing for efficiency, scalability, and robustness in dynamic environments.
• Develop methods for combining RL with other learning paradigms, such as supervised learning, unsupervised learning, and imitation learning, to improve the performance and generalization of autonomous systems.
• Work on reward engineering and exploration strategies for RL agents to enable fast and effective learning in real-world environments.
• Develop and implement simulation environments for training and evaluating RL and imitation learning algorithms, focusing on tasks such as navigation, manipulation, environment exploration and gait planning with dynamic environments.
• Design and evaluate approaches for transfer learning and domain adaptation to ensure that RL agents can transfer knowledge learned in one environment to new, unseen environments.
• Integrate RL and imitation learning algorithms into robotic platforms, ensuring they can function in real-time with the robot's sensors and actuators.
• Work closely with cross-functional teams, including robotics engineers, perception engineers, and software developers, to deploy decision-making algorithms in production environments.
• Perform thorough testing and validation of RL and imitation learning algorithms in both simulated i.e. Isaac Sim and real-world robotic systems i.e NVIDIAs Jetson hardware.
• Continuously monitor and improve the efficiency of learning algorithms, reducing training time and computational costs while maintaining high performance.
• Stay up-to-date with the latest research and advancements in reinforcement learning, imitation learning, and autonomous decision-making, applying relevant techniques to real-world applications.
• Contribute to the development of internal tools, frameworks, and libraries to support the deployment and scaling of RL algorithms in production systems.

Required Qualifications

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Robotics, or a related field.
• Strong background in Reinforcement Learning (RL) and Imitation Learning, with hands-on experience applying these techniques to real-world problems (3+ years of research or industrial experience).
• Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or JAX, with experience in building and training RL agents using Isaac Lab.
• In-depth understanding of RL algorithms, including Q-learning, Policy Gradient methods, Actor-Critic, and deep RL techniques (e.g., DQN, A3C, PPO).
• Experience with Imitation Learning algorithms, such as Behavioral Cloning, DAGGER, or GAIL, and applying them in autonomous systems.
• Strong programming skills in Python, C++, or similar languages commonly used in AI and robotics.
• Experience with simulation platforms like Isaac Sim (Preferred) ,Gazebo, Unity, or PyBullet for RL agent training and evaluation.
• Familiarity with robotic systems, sensors, and actuators, and how to integrate AI algorithms with these hardware components.
• Ability to communicate complex AI research findings and algorithmic designs clearly to both technical and non-technical stakeholders.
• Strong analytical and problem-solving skills, with the ability to debug and optimize complex machine learning algorithms.
• Experience working with large-scale datasets and parallel or distributed computing frameworks is a plus.

Preferred Qualifications

• Experience with multi-agent reinforcement learning or cooperative decision-making in autonomous systems.
• Knowledge of safe exploration techniques and reward design in RL.
• Familiarity with cloud computing and distributed training infrastructure for AI and RL algorithms (e.g., AWS, Google Cloud).
• Experience in deploying RL-based decision-making systems in real-world applications such as robotics, autonomous vehicles, or drones.
• Contributions to open-source AI or reinforcement learning libraries, or published research in top AI conferences or journals.

Please Send CVs at

[email protected]

[email protected]

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