Columbus, Ohio · el año pasado
Desarrollar e implementar sistemas de control y manipulación robótica basados en aprendizaje, incluyendo clonación conductual, aprendizaje por refuerzo, modelos de difusión y modelos visión-lenguaje-acción, para flotas de celdas robóticas industriales.
Build the Path Forward 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. Path built its business on autonomous welding, with robots deployed at customer sites across North America. Now our Robot Learning team is building what comes next: a general-purpose robotic foundation model for manufacturing, trained at scale with reinforcement learning and deployed on real factory floors. Why This Role Build a foundation model for manufacturing: Manufacturing demands far more: heavy parts, precision measured in millimeters, and steps that can’t be undone once they’re done. No existing model meets that bar, so we’re building one. You’ll help create a model that learns from real factory data and can take on new tasks and new parts without being reprogrammed each time. Scale RL: Imitation learning gets robots to human level. Our customers need more: faster cycle times, higher throughput, and precision humans can’t sustain. We’re scaling RL with massively parallel simulation, transferring policies to real industrial robots, and building toward one generalist model that learns from many specialist policies. You’ll work on open problems in RL and see the results run in production at customer sites. What You’ll Do • Train RL policies for high-precision manipulation in large-scale, GPU-parallel simulation. • Transfer policies from simulation to real industrial robots, using vision and force/torque sensing. • Scale from task-specific policies to generalist models that handle new parts without retraining. • Combine foundation models (VLAs, world models) with RL to solve long-horizon assembly tasks. • Deploy and evaluate policies on real robotic cells with customers, and use the results to improve the models. Who You Are • MS or PhD in Robotics, Computer Science, or a related field. • Hands-on experience applying RL to manipulation or contact-rich control. • Experience with sim-to-real transfer and GPU-parallel simulation (e.g., Isaac Lab, MuJoCo). • Experience deploying learned policies on physical robots. • Strong Python and PyTorch or JAX skills, including training at scale. Why You'll Love It Here • 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! Who We Are 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 HR@path-robotics.com. 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.
Publicada el 12 ago 2025
