Machine Learning Engineer, Perception

Build weld perception systems for embodied robotics, turning point cloud and sensor data into production-ready deep learning capabilities.

Responsibilities

• Implement, validate, and iterate on machine learning algorithms for weld perception tasks, including point cloud registration, seam detection, and joint geometry estimation, expanding coverage across joint types and part geometries.
• Build and maintain data pipelines for training and evaluating perception models, including annotated 3D scan ingestion, synthetic data generation, and structured dataset management for iterative improvement.
• Run rigorous model evaluation experiments with failure mode analysis, FP/FN 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; contribute to code and experiment reviews; maintain clear documentation of methods, parameters, and results.
• Lead research, development, and production deployment of advanced perception algorithms for 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 for during-weld seam tracking using sensor fusion and probabilistic state estimation (for example, Kalman filtering) to achieve robust weld performance.
• Define and own the end-to-end ML lifecycle: dataset design and annotation strategy, 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 versus real scan data usage.
• Mentor engineers across levels by providing technical leadership on perception systems and ML methodology.

Requirements

• Master's or Ph.D. in CS, Robotics, or a 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.
• Comfort integrating ML models into production robotics services within ROS-based architectures and containerized deployment environments.
• Senior/Staff only: Demonstrated track record leading end-to-end ML projects from dataset design through fleet deployment with rigorous go/no-go frameworks, plus experience architecting distributed training and hyperparameter optimization workflows.

Technologies

• Python, PyTorch
• Open3D
• ROS
• Kalman filtering
• PointNet++, GeoTransformer
• Transformer-based architectures
• Graph-based approaches
• Containerized deployment environments
• Structured light, RGB, stereo sensors

Benefits

• Daily free lunch to keep you fueled and connected with the team
• Flexible PTO
• 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

Location: Columbus, OH (onsite)

Experience: 3+ years (Experienced) / 7+ years (Senior/Staff)