Location: Sunnyvale,CA, USA
Staff Machine Learning Engineer, Robot Learning
Are you ready to tackle some of the biggest challenges in modern robotics?
Join a well-funded, groundbreaking team building robotics AI systems for industrial applications!
We're looking for a Staff Machine Learning Engineer with a passion for robotic manipulation, motion control, and planning. You'll work on innovative solutions that directly impact the future of industrial automation.
What You'll Do
• Lead the development of cutting-edge behavioral cloning and reinforcement learning (RL) algorithms for complex robotic tasks.
• Craft lifecycle strategies, including data collection and fleet learning.
• Develop, optimize, and deploy learning-based methods for motion control and planning on industrial robotic systems.
• Collaborate with hardware and software teams to integrate advanced robotics solutions.
• Test and validate your work in both simulations and real-world environments.
• Stay at the forefront of advancements in robotics, control theory, and planning algorithms.
Who You Are
• Ph.D. in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field.
• Published in top-tier conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, CoRL).
• Experienced in machine learning for robotic manipulation and fine-grained motor control.
• Proficient in behavioral cloning, reinforcement learning, and robotic simulation tools (e.g., Isaac Sim, Gazebo, MuJoCo).
• Skilled in C++ and Python, with a deep understanding of control theory, kinematics, and optimization.
• Proven track record of deploying scalable solutions for autonomous robotics.
• Strong problem-solving and communication skills with a collaborative mindset.
Sound like you? Please apply and well talk.
We are an equal opportunity employer:
All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other protected category under applicable laws.