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# Senior Machine Learning Engineer - Reinforcement Learning

- **Company:** Path Robotics
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, Reliability Engineering, Tensorflow, Reinforcement Learning, Pytorch, Deep Learning, Information Technology, Machine Learning Operations
- **Published:** November 4, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-reinforcement-learning--ba9a3a73-fac1-47ee-bdad-6038bceddabe

## About the Role

Reinforcement Learning, Machine Learning, Risk, Robotics, Continuous Improvement, Artificial Intelligence (AI), Reliability Engineering, Manufacturing, Physics, Welding, * 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.

## Description

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.

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., * Daily free lunch to keep you fueled and connected with the team