Senior Machine Learning Engineer, Neural Simulators

Path Robotics is building the future of embodied intelligence with AI-driven systems for robotics. They are seeking a Senior Machine Learning Engineer to lead the development of a neural welding simulator, focusing on creating a learned world model that captures the dynamics of welding for large-scale reinforcement learning training.

Responsibilities:

• Build a learned world model of the welding process that predicts future system behavior under robot actions
• Develop multimodal neural simulators incorporating signals such as 3D scans, video, thermal data, and electrical measurements
• Design, train, and evaluate large-scale generative or dynamics models (e.g., video prediction, latent world models, 3D or spatiotemporal representations) capable of long-horizon rollouts
• Collaborate with reinforcement learning engineers by integrating the neural simulator into RL pipelines for policy training and evaluation
• Run research tracks in parallel with production development, including hypothesis-driven experimentation and ablation
• Partner closely with data and MLOps teams to support scalable training, evaluation, and deployment - while remaining comfortable owning pieces of the stack when needed
• Translate research prototypes into robust, maintainable production code when they prove valuable
• Validate simulator performance against real-world robotic welding data and support sim-to-real transfer

Requirements:

• Experience building and deploying ML systems for robotics or other complex physical processes in real-world settings
• Hands-on experience with world models, learned simulators, video generation, 3D modeling, or dynamics prediction
• Comfortable training large models from scratch and working with the tooling and infrastructure required to scale experiments
• Enjoy working with messy, real-world data and are pragmatic about imperfect ground truth
• Strong software engineer with solid Python skills and experience in frameworks such as PyTorch or JAX
• You are excited by a role that blends research depth with practical impact, and you're willing to context-switch when the team needs it