AI Engineer - Reinforcement Learning (Senior)

Description

Reinforcement learning is transforming our robotic intelligence, enabling autonomous behavior without human guidance. We are seeking a Senior AI Engineer with deep expertise in reinforcement learning and deep learning, including supervised and self-supervised learning, to lead our engineering team. Your role will involve leveraging both simulated and real-world data to address practical challenges. If you are passionate about advancing AI and developing innovative solutions, join us in shaping the future of intelligent robotics.

Responsibilities

• Develop cutting-edge reinforcement learning algorithms to enable robots to autonomously execute motor commands based on raw sensor input.
• Design, test, and refine your algorithms to meet the demands of complex real-world locomotion, autonomy and manipulation tasks.
• Collaborate with the computer vision and imitation learning team to innovate methods that leverage both simulated and real-world data.
• Implement deployment-ready code for the real robot, optimized for the robot’s computational constraints.
• Build, lead and mentor an exceptional team of software engineers.
• Provide expert guidance to product managers and executives for strategic decision-making.
• Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.

Qualifications

• Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
• A minimum of five years of industry or research experience, with PhD experience applicable.
• Strong deep learning fundamentals, including supervised and self-supervised learning techniques, and reinforcement learning, including Markov Decision Processes (MDPs), neural network architectures, policy optimization algorithms, model-based vs. model-free RL, exploration-exploitation strategies, value function methods, transfer learning, domain adaptation, sim-to-real transfer, etc.
• Strong background in robotics including autonomy and/or manipulation.
• Experience with deploying artificial neural networks on hardware platforms.
• Ability to write production-level code in modern C++.
• Ability to prototype algorithms and train deep neural networks in Python.
• PhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.
• Publications at top-tier conferences.
• Experience in managing a software team.