Senior Machine Learning Engineer (Reinforcement Learning/World Model)

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

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.

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.

Who You Are

• 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.