AI Research Engineer

As an AI Research Engineer at our company, you will be at the forefront of 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 collaborate with a team of AI researchers and engineers to innovate in the field of autonomous systems. Key Responsibilities: - Conduct research and development in Reinforcement Learning (RL) to enable robots to learn complex tasks through 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. - Combine RL with other learning paradigms to enhance the performance and generalization of autonomous systems. - Design simulation environments for training and evaluating RL and imitation learning algorithms for various tasks. - Collaborate with cross-functional teams to deploy decision-making algorithms in production environments. - Enhance the efficiency of learning algorithms to reduce training time and computational costs. - Contribute to the development of internal tools, frameworks, and libraries to support the deployment and scaling of RL algorithms. Qualifications Required: - Ph.D or Masters degree in Computer Science, Artificial Intelligence, Robotics, or related field. - Strong background in Reinforcement Learning with 3+ years of research or industrial experience. - Experience with CUDA, PyTorch, TensorFlow, or JAX. - Proficiency in C++, Python, and AI model optimization. Additionally, experience in the following areas will be advantageous: - Proficiency in machine learning frameworks with experience in building and training RL agents using Isaac Lab and Omniverse APIs. - Understanding of RL algorithms like Q-learning, Policy Gradient methods, Actor-Critic, and deep RL techniques. - Experience with Imitation Learning algorithms and applying them in autonomous systems. - Familiarity with simulation platforms like Isaac Sim, Gazebo, Unity, or PyBullet. - Knowledge of robotic systems, sensors, actuators, and integrating AI algorithms with hardware components. - Experience with large-scale datasets, parallel or distributed computing frameworks. - Working with multi-agent reinforcement learning or cooperative decision-making. - Knowledge of safe exploration techniques and reward design in RL. - Familiarity with cloud computing and distributed training infrastructure for AI and RL algorithms. - Experience in deploying RL-based decision-making systems in real-world applications. As an AI Research Engineer at our company, you will be at the forefront of 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 collaborate with a team of AI researchers and engineers to innovate in the field of autonomous systems. Key Responsibilities: - Conduct research and development in Reinforcement Learning (RL) to enable robots to learn complex tasks through 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. - Combine RL with other learning paradigms to enhance the performance and generalization of autonomous systems. - Design simulation environments for training and evaluating RL and imitation learning algorithms for various tasks. - Collaborate with cross-functional teams to deploy decision-making algorithms in production environments. - Enhance the efficiency of learning algorithms to reduce training time and computational costs. - Contribute to the development of internal tools, frameworks, and libraries to support the deployment and scaling of RL algorithms. Qualifications Required: - Ph.D or Masters degree in Computer Science, Artificial Intelligence, Robotics, or related field. - Strong background in Reinforcement Learning with 3+ years of research or industrial experience. - Experience with CUDA, PyTorch, TensorFlow, or JAX. - Proficiency in C++, Python, and AI model optimization. Additionally, experience in the following areas will be advantageous: - Proficiency in machine learning frameworks with experience in building and training RL agents using Isaac Lab and Omniverse APIs. - Understanding of RL algorithms like Q-learning, Policy Gradient methods, Actor-Critic, and deep RL techniques. - Experience with Imitation Learning algorithms and applying them in autonomous systems. - Familiarity with simulation platforms like Isaac Sim, Gazebo, Unity, or PyBullet. - Knowledge of robotic systems, sensors, actuators, and integrating AI algorithms with hardware components. - Experience with larg