Machine Learning Engineer, Robot Learning

At Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.

Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.

We are seeking a skilled and motivated 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 experience 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.

• Work on the development and deployment of behavioral cloning (BC), reinforcement learning (RL), and diffusion models to solve complex robotic tasks.
• Leverage multi-modal data, including image-based data, force, and torque to enhance robotic manipulation and control.
• Design and implement vision-language-action (VLA) models for more intelligent and responsive robotic systems.
• Craft strategies for the entire solution's lifecycle, including data collection, fleet learning, and continuous learning.
• Develop, optimize, and deploy learning-based methods for robot control and object manipulation algorithms on a fleet of industrial robotic cells.
• Collaborate with hardware and software teams to integrate manipulation, control, and planning algorithms into robotic platforms.
• Stay updated with the latest advancements in robotics, control theory, and planning algorithms.

• Ph.D. or Master’s degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field.
• Proven track record in publishing at top conferences (e.g., NeurIPS, ICLR, ICML, CoRL, RLC).
• Experience with robotic simulation tools (e.g., Isaac Sim, MuJoCo).
• Experience with machine learning techniques applied to robot manipulation.
• Experience building models from scratch and deploying them in real-world applications.
• Experience with pose estimation, segmentation, active perception, and affordance-based bin picking.
• Strong programming skills in languages such as C++ or Python.
• Experience with robotic simulation tools (e.g., Isaac Sim, MuJoCo).
• Proven track record of deploying scalable solutions for autonomous robotic systems.
• Strong communication skills, with the ability to convey complex technical concepts to a diverse audience.

• Daily free lunch to keep you fueled and connected with the team
• Flexible PTO so you can take the time you need, when you need it
• Comprehensive medical, dental, and vision coverage
• 6 weeks fully paid parental leave, plus an additional 6–8 weeks for birthing parents (12–14 weeks total)
• 401(k) retirement plan through Empower
• Generous employee referral bonuses—help us grow our team!

At 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.

If you require a reasonable accommodation to participate in the application process or any part of the hiring process, please contact [email protected]. We are committed to providing equal access and will work with qualified individuals to ensure a fair and accessible hiring experience. We will respond to your request within 48 hours.