> Markdown version of [/jobs/ext/2716452-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2716452-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer

- **Company:** Path Robotics
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Tensorflow, Reinforcement Learning, Pytorch, Deep Learning, Information Technology, Machine Learning Operations
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-engineer-reinforcement-learning-world-model-path-robotics-8742565

## About the Role

* 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

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


## Related Videos

- [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning)
- [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Robots are coming into the wild! Full-Stack Robotics Engineers, be ready!](https://www.wearedevelopers.com/videos/479-robots-are-coming-into-the-wild-full-stack-robotics-engineers-be-ready)
- [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery)
- [30 Golden Rules of Deep Learning Performance](https://www.wearedevelopers.com/videos/11-30-golden-rules-of-deep-learning-performance)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)