---
title: 'Senior Machine Learning Engineer (Reinforcement Learning/World Model) at Path Robotics'
canonical: 'https://feeny.ai/job/senior-machine-learning-engineer-reinforcement-learning-world-model-path-ka233vdcrm82'
type: 'job'
last_seen: '2026-11-11'
---

# Senior Machine Learning Engineer (Reinforcement Learning/World Model) at Path Robotics

- **Company:** Path Robotics
- **Location:** Columbus, OH
- **Work type:** remote
- **Posted:** 2026-07-13
- **Last confirmed live:** 2026-11-11
- **Apply:** https://boards.greenhouse.io/pathrobotics/jobs/8632104002?gh_jid=8632104002

## Job description

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.

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.

## 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.

## Why You'll Love It Here

- 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 6–8 weeks for birthing parents (12–14 weeks total)
- 401(k) retirement plan through Empower
- Generous employee referral bonuses—help us grow our team!

## Who We Are

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 HR@path-robotics.com. 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.

## About Path Robotics

## Company Overview
- **One-liner**: Path Robotics builds physical AI systems that enable autonomous robotic welding for heavy industries, addressing the critical skilled labor shortage in manufacturing.
- **Entity Type**: Private (Series D, $271M total funding)
- **Headquarters**: Columbus, Ohio, United States
- **Founded**: 2014
- **Founders**: Andy Lonsberry (CEO) and Alex Lonsberry (CTO)

## Core Business
- **Primary industry**: Industrial robotics, AI-powered manufacturing automation, heavy fabrication
- **Target customers**: B2B, Enterprise — specifically heavy industry, defense, utility & energy, data center construction, and general manufacturing
- **Mission**: Create real value for customers while rebuilding the backbone of American industry by helping manufacturers navigate the skilled labor shortage.

## Products & Services
- **Intelligent Welding Cells**: Fixed robotic cells that use AI and computer vision to see, understand, and adapt to part variations in real-time, enabling autonomous welding without precision fixturing.
- **Obsidian™ AI**: A foundational AI model for welding, trained on tens of millions of welded inches, enabling real-time adaptation for high-quality welds across part-to-part variations.
- **JobBuilder**: A web-based software application launched at FABTECH 2024 that simplifies part programming, reducing the setup bottleneck and expanding the addressable market to fabricators without specialized robotics staff.
- **Path Foundry (RaaS)**: A Robotics-as-a-Service model where Path owns and operates the welding cells, charging customers for welding capacity with $0 CapEx, targeting a 30% lower cost and 4x productivity improvement.
- **Rove**: A mobile welding system mounted on a quadruped platform (announced April 2026), extending adaptive welding capabilities beyond fixed-cell applications.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: $271M total funding raised (Series D, last raised ~2 years ago)
- **Notable Investors/Partners**: Not publicly named in available sources; the company has a Board of Directors and leadership team including a Chief Revenue Officer (Heather Carroll) and EVP of Global Operations (Mike Renn).
- **Growth Signals**: 198 employees as of March 2026; launched Path Foundry RaaS model in October 2024; launched JobBuilder at FABTECH 2024; announced Rove mobile welding system in April 2026; serves 4 key industries (Defense, Data Centers, Utility & Energy, Heavy Industry); addresses a forecasted 600k welder shortage by 2030.

## Competitive Advantages
- **Perception-driven fit-up**: The core differentiator is the ability to recognize part geometry, compensate for dimensional variation, and execute a weld without precision fixturing — a capability traditional robotic welding lacks, which requires tight part consistency.
- **Obsidian™ foundational AI model**: A purpose-built, first-of-its-kind AI model for welding trained on tens of millions of welded inches, enabling continuous learning and real-time adaptation.
- **RaaS model (Path Foundry)**: Eliminates upfront CapEx for customers, lowering adoption barriers for mid-market fabricators who cannot justify six- or seven-figure capital commitments.
- **End-to-end autonomy**: The system handles pick, fit-up, and weld in a single automated flow, with 24/7 mission control support, targeting 4x productivity improvement.

## Strategic Focus
- **Scaling the RaaS model**: Path Foundry is the primary go-to-market strategy, requiring the company to manage fleet maintenance, logistics, service-level guarantees, and asset utilization across a distributed installed base.
- **Expanding addressable geometry**: The Rove mobile platform announcement signals a push beyond fixed-cell applications into more varied and mobile welding environments.
- **Defense and critical infrastructure**: The company is actively targeting defense (shipyards, arsenals), data center construction, and utility/energy sectors as key growth verticals.
- **Reducing programming friction**: JobBuilder aims to lower the barrier to adoption for fabricators without specialized robotics staff, expanding the total addressable market.

## Why Work Here
- **Impactful mission**: The company directly addresses a critical national problem — a forecasted 600,000 welder shortage by 2030 — by building technology that keeps American manufacturing competitive.
- **Cutting-edge technology**: Work at the intersection of AI, machine learning, computer vision, and robotics, building a foundational AI model (Obsidian) trained on tens of millions of welded inches.
- **Collaborative culture**: The company emphasizes "People + Robots Together" and states that "Our people are our competitive advantage," with a focus on creating real customer value.
- **Growth trajectory**: With $271M in funding, 198 employees, and a pivot to a RaaS model, the company is in a high-growth phase with significant runway.
- **Leadership**: Founded and led by brothers Andy Lonsberry (CEO) and Alex Lonsberry (CTO), who have scaled the company from a university basement to a leading physical-AI company.
- **Work environment**: Based in Columbus, Ohio, with a focus on collaborative, passionate teams working on cutting-edge machine learning and impactful real-world problems. The careers page highlights "Impactful," "Collaborative," "AI-Powered," and "Cutting-edge machine learning" as core themes.

## Sources
1. [path-robotics.com](https://www.path-robotics.com/)
2. [path-robotics.com/careers](https://www.path-robotics.com/careers)
3. [path-robotics.com/about](https://www.path-robotics.com/about)
4. [cbinsights.com](https://www.cbinsights.com/company/path-robotics)
5. [robotics.press](https://robotics.press/news/path-robotics-company-profile/)

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