[Hiring] Senior Machine Learning Engineer @Path Robotics

Role Description

We are seeking a Senior Machine Learning Engineer to lead the development of a neural welding simulator - a learned world model that captures the visual and physical dynamics of welding and enables large-scale RL training. This role sits at the intersection of generative modeling, robotics, and applied physics. It is research-heavy by design, while still grounded in production reality.

Senior ML Engineer, World Models

What You’ll Do:

• 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

What You’ll Do:

• 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.

Qualifications

• 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!