Machine Learning Engineer – Perception

🕒 April 8

🏈 Ohio – Remote

🏈 Ohio – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

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Path Robotics

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2014

🤖 Artificial Intelligence

🔧 Hardware

💰 $100M Series C on 2021-07

Manufacturing • Artificial Intelligence • Hardware

Path Robotics is a company that specializes in advanced robotic welding systems designed to optimize manufacturing processes with precision and efficiency. They offer Robots-as-a-Service (RaaS), eliminating the need for significant upfront capital expenditure. Their AI-driven robotic systems learn and adapt to improve welding performance over time. Path Robotics' solutions are integrated seamlessly into existing production lines, working with materials like carbon steel, stainless steel, and aluminum. They cater to various industries, including transportation, manufacturing, and construction, providing unmatched precision and adaptability to help businesses achieve their production goals.

📋 Description

• 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 rigorous model evaluation experiments, including failure mode analysis, FP/FN rate characterization, and benchmarking against quantitative registration accuracy thresholds, and communicate findings clearly 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 actively in code and experiment reviews; and maintain clear documentation of methods, parameters, and results. • 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. • Design and lead experiments evaluating state-of-the-art deep learning models, including transformer-based and geometric feature learning architectures. • Design and lead real-time perception systems such as during-weld seam tracking, applying sensor fusion with probabilistic state estimation (e.g., Kalman filtering) to achieve robust weld performance. • Define and own the end-to-end ML lifecycle, from dataset design and annotation strategy through training, benchmarking, and fleet deployment, with clear go/no-go evaluation frameworks. • Architect distributed training and hyperparameter optimization workflows; drive strategy for data acquisition, annotation tooling, and synthetic vs. real scan data usage. • Mentor engineers across levels, providing technical leadership on perception systems and ML methodology.

🎯 Requirements

• Master's or Ph.D. in CS, Robotics, or related field (Computer Vision, ML, or Perception); Bachelor's with strong production ML experience also considered. • 3+ years (Experienced) / 7+ years (Senior/Staff) in real-world robotics or industrial ML. • 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 like 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. • Comfortable integrating ML models into production robotics services within ROS-based architectures and containerized deployment environments. • Senior/Staff: Demonstrated track record leading end-to-end ML projects from dataset design through fleet deployment with rigorous go/no-go frameworks; experience architecting distributed training and hyperparameter optimization workflows

🏖️ Benefits

• 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!