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Path Robotics

Autonomous robotic welding systems provider

Machine Learning Engineer - Perception

Computer VisionConfirmed live in the last 24 hours
Full-timeColumbus, OH, USAIn-person

About the Job

Requirements

  • A Master's or Ph.D. in Computer Science, Robotics, or a related field (Computer Vision, machine learning, or Perception); a Bachelor's degree with strong production machine learning experience is also considered.
  • Three or more years of experience in real-world robotics or industrial machine learning for Experienced openings, or seven or more years for Senior/Staff openings.
  • Strong Python fluency and hands-on PyTorch experience, including training, evaluating, and deploying deep learning models in production.
  • Experience with 3D point cloud data and libraries such as Open3D, including geometric concepts such as surface segmentation, spatial queries, and point-wise labeling.
  • Familiarity with 3D deep learning architectures such as PointNet++, GeoTransformer, or similar transformer-based or graph-based approaches on geometric data.
  • Ability to integrate machine learning models into production robotics services within ROS-based architectures and containerized deployment environments.
  • For Senior/Staff openings, a demonstrated track record leading end-to-end machine learning projects from dataset design through fleet deployment with rigorous go/no-go frameworks, and experience architecting distributed training and hyperparameter optimization workflows.

Responsibilities

  • Implement, validate, and iterate on machine learning algorithms for weld perception tasks, including point cloud registration, seam detection, and joint geometry estimation, progressively expanding coverage across joint types and part geometries.
  • Build and maintain data pipelines for training and evaluating perception models, spanning annotated 3D scan data ingestion, synthetic data generation, and structured dataset management for iterative model improvement.
  • Run model evaluation experiments, including failure mode analysis, false-positive and false-negative rate characterization, and benchmarking against quantitative registration accuracy thresholds; communicate findings to guide next steps.
  • Integrate trained models into production ROS-based robotics services, ensuring low-latency inference and compatibility with deployed cell configurations.
  • Write clean, well-tested Python code; participate in code and experiment reviews; and maintain clear documentation of methods, parameters, and results.
  • For Senior/Staff openings, lead research, development, and production deployment of advanced perception algorithms spanning point cloud registration, seam detection, and real-time in-process tracking across structured light, RGB, and stereo sensors.
  • For Senior/Staff openings, design and lead experiments evaluating state-of-the-art deep learning models, including transformer-based and geometric feature learning architectures.
  • For Senior/Staff openings, design and lead real-time perception systems, such as during-weld seam tracking, applying sensor fusion with probabilistic state estimation such as Kalman filtering to achieve robust weld performance.
  • For Senior/Staff openings, define and own the end-to-end machine learning lifecycle, from dataset design and annotation strategy through training, benchmarking, and fleet deployment, with clear go/no-go evaluation frameworks.
  • For Senior/Staff openings, architect distributed training and hyperparameter optimization workflows; drive strategy for data acquisition, annotation tooling, and synthetic versus real scan data usage.
  • For Senior/Staff openings, mentor engineers across levels and provide technical leadership on perception systems and machine learning methodology.

About the Company

Path Robotics

Autonomous robotic welding systems provider

Path Robotics builds autonomous robotic welding systems for manufacturers. Its welding cells run on Obsidian, a physical AI model for welding trained inside the Weld World Model, a neural network simulator created from tens of millions of welded inches of fleet weld data. Cameras and lasers around the torch scan each part, identify seams, plan weld paths, and adapt in real time to part variation and imperfect fits, with no programming required by the user. The product line includes the AW-3 cell for large parts, the AF-1 cell that autonomously picks, fits up and welds small parts, and Rove, a mobile welding robot. Customers are fabricators and manufacturers with high-mix production, with focus on defense and shipbuilding, utility and energy infrastructure, and data center structures. Path offers its cells as robots-as-a-service with no upfront capex, a recurring fee, and 24/7 remote mission control support.

Company size
201-500
Company stage
Series E
Total funding
$589.7M
Headquarters
Columbus, OH
Founded
2014

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