Master's or Ph.D. in Computer Science, Robotics, or related field; Bachelor's with strong ML experience considered
3+ years (Experienced) / 7+ years (Senior/Staff) of experience in robotics or industrial machine learning
Proficient in Python and hands-on experience with PyTorch for deploying deep learning models
Experience with 3D point cloud data libraries such as Open3D
Understanding of geometric concepts like surface segmentation and spatial queries
Experience integrating ML models into ROS-based robotics services
Proven track record in leading end-to-end ML projects, for Senior/Staff roles.
Responsibilities
Implement and improve machine learning algorithms for weld perception tasks
Build and maintain data pipelines for perception model training
Conduct rigorous model evaluation experiments and communicate findings
Integrate models into production ROS-based robotics services
Write clean and tested Python code, engaging in code reviews and documentation
Lead deployment of advanced perception algorithms and real-time systems
Define and manage the entire ML lifecycle, from dataset design to deployment.
Benefits
Daily free lunch to foster team connection
Flexible PTO for personal time off
Comprehensive medical, dental, and vision coverage
6 weeks fully paid parental leave; up to 14 weeks for birthing parents
401(k) retirement plan availability
Generous employee referral bonuses to encourage team growth.
Full Job Description
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
Why You'll Love Working 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!
