Tesseract - AI Research Engineer - Reinforcement Learning

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 NVIDIA's 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 : - Bachelor's or Master's 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. (ref:hirist.tech)

Location: mumbai, IN

Posted Date: 5/8/2025