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) to enable robots to learn complex tasks through both trial-and-error and expert demonstrations

- Develop and train GPU-optimized models for real-time robotic control using voice to action workflows

- Utilize NVIDIA TAO Toolkit and TensorRT for AI model deployment in simulations and real 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.

- 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.

- Work closely with cross-functional teams, including robotics engineers, perception engineers, and software developers, to deploy decision-making algorithms in production environments.

- Continuously monitor and improve the efficiency of learning algorithms, reducing training time and computational costs while maintaining high performance.

- Contribute to the development of internal tools, frameworks, and libraries to support the deployment and scaling of RL algorithms in production systems.

Required Qualifications :

- Ph.D or Masters degree in Computer Science, Artificial Intelligence, Robotics, or a related field.

- Strong background in Reinforcement Learning with hands-on experience applying these techniques to real-world problems (3+ years of research or industrial experience).

- Experience with CUDA, PyTorch, TensorFlow or JAX

- Proficiency in C++, Python, and AI model optimization

Preferred Qualifications :

- Proficiency in machine learning frameworks with experience in building and training RL agents using Isaac Lab and Omniverse APIs.

- 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.

- 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.

- Experience working with large-scale datasets and parallel or distributed computing frameworks is a plus.

- 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.