ML Engineer RL (Reinforcement Learning)

Senior Machine Learning Engineer (Reinforcement Learning)

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The Opportunity: Scaling Production-First Physical AI

While 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.

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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.

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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.

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* 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.

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* Master's or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical

experience.