Machine Learning Engineer – Perception
Path Robotics
full-time
Posted on:
Location Type: Remote
Location: Ohio • United States
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About the role
- 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!
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learning algorithmspoint cloud registrationseam detectionjoint geometry estimationdata pipelinesPythonPyTorchdeep learning modelssensor fusionhyperparameter optimization
Soft Skills
technical leadershipmentoringcommunicationexperiment designproblem-solving
Certifications
Master's in Computer SciencePh.D. in Robotics