Path Robotics - Senior Machine Learning Engineer (Reinforcement Learning/World Model)

• Build action-conditioned world models that predict how the welding process evolves under changes to robot motion and process parameters. • Model relationships among inputs, system state, physical dynamics, and resulting weld quality. • Develop multimodal models using data such as video, 3D scans, thermal measurements, electrical signals, robot state, and process parameters. • Explore latent dynamics, video prediction, generative modeling, and spatiotemporal representations. • Improve long-horizon rollout accuracy, physical plausibility, temporal consistency, and computational efficiency. • Quantify model uncertainty and identify conditions under which predictions are unreliable. • Validate learned predictions against real-world welding data. • Integrate the model into RL, planning, process-optimization, evaluation, and synthetic-data workflows. • Prevent downstream optimization systems from exploiting inaccuracies in the learned model. • Translate promising research into scalable training and inference systems. • Senior ML Engineer, Reinforcement Learning • Develop reinforcement learning approaches for optimizing welding decisions and process outcomes. • Define state, observation, action, and reward representations based on measurable manufacturing objectives. • Train and evaluate policies using learned world models, traditional simulation, offline datasets, and controlled real-world experiments. • Develop offline, model-based, or constrained RL methods suitable for limited and expensive physical interaction. • Optimize across competing objectives such as weld quality, cycle time, reliability, energy use, and equipment constraints. • Design methods that account for uncertainty, distribution shift, delayed outcomes, and sparse or imperfect reward signals. • Diagnose reward exploitation, unsafe behavior, policy instability, and model exploitation. • Establish reliable offline and real-world policy evaluation methods. • Partner with controls, welding, robotics, world-model, data, and ML infrastructure engineers. • Translate research prototypes into dependable training, evaluation, and deployment systems.