Path Robotics - Senior Machine Learning Engineer, Neural Simulators
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. • A Note on Background • We do not expect prior welding experience. We do expect comfort modeling complex physical systems from data, reasoning about failure modes, and iterating toward accuracy under real-world constraints. • do not
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.
Benefits
• You’ll help define the core learning infrastructure that underpins our robotic systems • You’ll have space to pursue research tracks that run in parallel with production development. • You’ll work on problems where success is measurable in the physical world - not just benchmarks. • You’ll join a team that values depth, ownership, and adaptability over narrow specialization. • 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!
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