Machine Learning Engineer

Senior Machine Learning Engineer (Reinforcement Learning) The Opportunity: Scaling Production-First Physical AIWhile much of the robotics industry is focused on research and lab prototypes, our client is a production-first leader in Embodied Intelligence. They have 50+ autonomous robotic cells already live in heavy manufacturing environments, solving complex physics problems that traditional simulators struggle to capture.We are looking for a Senior ML Engineer, Reinforcement Learning to act as a key architect for a proprietary neural world model. This is a hands-on IC role where you will develop and deploy policies that enable robots to learn, predict, and plan by replacing or augmenting classical physics simulators with fast, high-fidelity learned ones.The Role You will define the technical direction for reinforcement learning approaches to optimize welding decisions and process outcomes. Your focus will be moving beyond digital benchmarks to ensure industrial systems perform with precision in real-world manufacturing conditions, where physical interaction is limited and expensive.Policy & Representation: Define state, observation, action, and reward representations based on measurable manufacturing objectives; develop offline, model-based, or constrained RL methods.World Model Training: Train and evaluate policies using learned world models, traditional simulation, and offline datasets to optimize across competing objectives like weld quality and cycle time.Production Excellence: This isn’t a lab role. You will diagnose reward exploitation, unsafe behavior, and policy instability, translating research prototypes into dependable deployment systems.Bridge the Gap: Design methods that account for the messiness of the real world—specifically handling uncertainty, distribution shift, delayed outcomes, and sparse reward signals.Who You AreMaster's or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practicalexperience.Experience: You have a proven track record of developing and deploying reinforcement learning algorithms on real-world physical systems.Technical Depth: Proficiency in Python and deep learning frameworks (PyTorch or TensorFlow) is non-negotiable; experience with simulation environments like MuJoCo or Isaac Gym is strongly preferred.Domain Depth: Solid understanding of probability, statistics, and optimization, with the ability to navigate the "bias toward shipping" from research to production.AI-Integrated: You understand the current shift toward learning-based robotics and have experience (or a strong interest) in training policies that handle complex, poorly modeled physics.Collaborative Architect: You have a proven ability to partner across functional teams to establish reliable evaluation methods.Logistics Compensation: The company pays NY/SF market rates, providing a significant quality-of-life advantage.Location: This role is fully remote (or onsite in Columbus, OH, if preferred).Visa: Full support for H1B transfers and Green Card sponsorship (Note: UK candidates must already possess a valid US work visa).Why Join? You’ll be joining a culture built on humility and collaboration. This is a rare chance to work at the intersection of classical robotics and Physical AI, seeing your RL policies solve massive industrial challenges in real-time.