Note: The job is a remote job and is open to candidates in USA. Path Robotics is building AI-driven robotic systems that learn and adapt in real-world manufacturing environments. The Senior Machine Learning Engineer will develop neural world models and reinforcement learning systems for welding, integrating research into reliable training, evaluation, and deployment workflows.
Responsibilities
- 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
- 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
Skills
- Master's or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience
- Experience developing and deploying reinforcement learning algorithms on real-world systems
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience with simulation environments (e.g., MuJoCo, Isaac Gym)
- Solid understanding of probability, statistics, and optimization
- Experience with training and deploying ML models in production systems
Benefits
- 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!
Company Overview
Company H1B Sponsorship