Robot learning engineer

We are an early‑stage robotics startup working on building multi‑purpose mobile robots that can do complex manipulation tasks. We are looking for a creative, skilled, and motivated robot learning engineer to join our team in advancing robot manipulation capabilities. We are looking for people with proven expertise in machine learning and/or robotics. You will collaborate with a team of talented researchers and engineers, and drive ongoing innovation and technological advancements within the company.

This is a full‑time on‑site role in Santa Clara, CA.

• Design and implement state‑of‑the‑art learning algorithms for robot manipulation, navigation, and control—from simulation to deployment on physical systems
• Develop novel approaches to enhance robot dexterity and mobility using reinforcement learning, imitation learning, and foundation models, etc.
• Scale ML systems for large‑scale model training and fine‑tuning.
• Build diverse, robust manipulation skills that push the boundaries of what robots can do.
• Collaborate closely with hardware, controls, and systems engineers to create integrated solutions.

• PhD in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or related field; OR Master’s degree with 1+ years industry experience; OR Bachelor’s degree with 3+ years industry experience.
• 2+ years of hands‑on experience developing AI systems for robotics applications.
• Deep expertise in modern robot learning techniques (reinforcement learning, imitation learning, behavior cloning, etc.).
• Strong proficiency in Python and deep learning frameworks (PyTorch, Tensor Flow, or JAX).
• Proven experience conducting real robot experiments and debugging complex robotic systems.
• Experience with robot simulators (Isaac Gym, Isaac Sim, Mu Jo Co , SAPIEN, Drake, or similar).
• Excellent problem‑solving abilities and strong communication skills.
• Genuine passion for robotics and building products that work in the real world.

• Publications at top robotics/ML conferences (RSS, CoRL, ICRA, IROS, NeurIPS, ICLR, etc.).
• Experience with vision‑language models or foundation models for robotics.
• Familiarity with sim‑to‑real transfer techniques and domain randomization.
• Experience with distributed training and MLOps infrastructure.
• Background in manipulation, grasping, or mobile manipulation.
• Track record of taking research from prototype to production.