(Senior) AI Engineer - Reinforcement Learning Manipulation

What you’ll be doing

Develop cutting-edge reinforcement learning algorithms to enable robust, contact-rich dexterous manipulation, translating vision, depth, tactile, and proprioceptive sensor input into precise end-effector and joint-level motor commands.

Design, test, and refine algorithms to solve complex real-world manipulation challenges, such as handling diverse package form factors, dynamic hand-offs, and operating door handles or latches.

Collaborate with the foundation model team to innovate methods that leverage both simulated and real-world data.

What you must have

Strong background in robotic manipulation, including dynamics, grasp synthesis, and trajectory optimization.
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.

Get some bonus points

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 (e.g., ICRA, IROS, CoRL, RSS) specifically focusing on robotic manipulation, grasping, or contact-rich RL.

Demonstrated experience working with tactile sensing, multi-fingered robotic hands, or bimanual manipulation.