Senior Machine Learning Engineer (Reinforcement Learning/World Model)

About the role

Build the Path Forward

At Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.

Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.

Manufacturing demands exceptionally high performance, reliability, and adaptability. Processes like welding involve fast, complex, and poorly modeled physics that traditional simulators struggle to capture - especially in the long tail of real-world conditions.

We are building intelligent robotic systems that learn directly from data by combining neural world models with reinforcement learning. Our goal is to give robots the ability to learn, predict, and plan in complex manufacturing environments by replacing or augmenting classical physics simulators with fast, high-fidelity learned ones.

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.

We are hiring two Senior Machine Learning Engineers with complementary specializations:

- Senior Machine Learning Engineer, World Models

- Senior Machine Learning Engineer, Reinforcement Learning

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.