Senior Machine Learning Engineer (Reinforcement LearningWorld Model)

Build the Path Forward

At Path Robotics were 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

• 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.

• Masters 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.

• 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 68 weeks for birthing parents (1214 weeks total)
• 401(k) retirement plan through Empower
• Generous employee referral bonuseshelp us grow our team!

At Path Robotics we love coming to work to solve interesting and tough challenges but also because our ideas are welcomed and valued. We encourage unique thinking and are dedicated to creating a diverse and inclusive environment. All qualified applicants will receive consideration for employment without regard to race color religion gender gender identity or expression sexual orientation national origin genetics disability age or veteran status.

If you require a reasonable accommodation to participate in the application process or any part of the hiring process please contact We are committed to providing equal access and will work with qualified individuals to ensure a fair and accessible hiring experience. We will respond to your request within 48 hours.