Machine Learning Engineer, Perception

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.

We're seeking passionate ML Engineers to join our team at the intersection of welding science and artificial intelligence. We currently have experienced (L3/L4), senior (L5) and staff (L6) level openings within and you'll be instrumental in developing robotic welding solutions. You'll use your skills in computer vision, deep learning, and Python programming to tackle challenges in our field alongside our talented teams.

What You'll Do

Experienced

• 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.

Senior/Staff

• 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.

Who You Are

• 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