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Staff Machine Learning Engineer, Robot Learning

Build the Path ForwardAt Path Robotics, we’re attacking a trillion dollar opportunity - doing things that have never been done before to support an industry hurting from a lack of skilled labor. Big, hard problems are what Path tackles every day and our people are our greatest asset to get that job done. Our intelligent, hardworking team of people do the impossible every single day, yet remain incredibly kind, humble, and always ready to support one another.We are seeking a skilled and motivated Staff Machine Learning Engineer with expertise in modern robotics and manipulation to join our Robot Learning team to build agentic AI systems for industrial applications. The ideal candidate will have a strong background in robotics, with a focus on developing and implementing advanced manipulation techniques, motion control systems, and planning algorithms for autonomous robots. This role involves working closely with cross-functional teams to design, test, and deploy innovative solutions that improve the performance and capabilities of our robotic systems.This role can be located in our Bay Area or Columbus, Ohio offices.What You’ll Do• Lead the development and deployment of behavioral cloning and reinforcement learning (RL) algorithms to solve complex robotic tasks• Craft strategies for the entire solution's lifecycle including but not limited to data collection and fleet learning learning.• Develop, optimize, and deploy learning-based methods for motion control and planning algorithms on a fleet of industrial robotic cells.• Collaborate with hardware and software teams to integrate manipulation, control, and planning algorithms into robotic platforms.• Conduct simulations and real-world testing to validate the performance of manipulation, control, and planning algorithms.• Analyze and troubleshoot issues related to manipulation, motion control, and planning in robotic systems.• Stay updated with the latest 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.• Proven track record in publishing at top conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, CoLR).• Extensive experience with machine learning techniques applied to fine-grained motor control and robotic manipulation.• Extensive expertise in robotic manipulation, behavioral cloning, and reinforcement learning techniques.• Strong programming skills in languages such as C++ or Python.• Experience with robotic simulation tools (e.g., Isaac Sim, Gazebo, MuJoCo).• Solid understanding of control theory, kinematics, dynamics, and optimization techniques for robotics.• Familiarity with sensors, actuators, and robotic hardware components.• Proven track record of deploying scalable solutions for autonomous robotic systems.• Proven track record of publishing impactful work in the robotics field.• Excellent problem-solving skills and the ability to work both independently and in a team environment.• Strong communication skills, with the ability to convey complex technical concepts to a diverse audience.Who We AreAt Path Robotics we love coming to work to solve interesting and tough challenges but also because our ideas are welcomed and valued. We encourage unique thinking and are dedicated to creating a diverse and inclusive environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.Why You'll Love It Here• Free lunch every day• Flexible PTO• Medical, Dental and Vision insurance with 100% coverage on monthly medical premiums for you and any dependents on select plans• 6 weeks 100% paid parental leave plus an additional 6-8 weeks maternity leave for the birthing parent (12-14 weeks total)• 401K through Empower• Paid Referral Bonus
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Enabling robots to build so humans can create

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Full-time, on-site
DATE POSTED
November 23, 2024

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