Company Description
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 engineers 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.
Responsibilities
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
Qualifications
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, TensorFlow, or JAX)
Proven experience conducting real robot experiments and debugging complex robotic systems
Experience with robot simulators (Isaac Gym, Isaac Sim, MuJoCo, 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
Preferred Qualifications
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