Machine Learning Engineer, Perception

United StatesRemoteFull-timemid

Machine Learning EngineerData

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Requirements Summary

Master's or Ph.D. in Computer Science, Robotics, Computer Vision, Machine Learning, or a related technical field.

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Machine Learning EngineerData

This is an opportunity to develop advanced AI and computer vision systems that enable industrial robots to perceive, understand, and interact reliably with the physical world. You will work at the intersection of machine learning, robotics, and welding science, building perception technologies that help automate complex manufacturing processes. Depending on your experience level, you may focus on implementing and optimizing models or lead the architecture and deployment of sophisticated perception systems. Your work will span 3D point cloud processing, deep learning, sensor fusion, and real-time robotic applications. You will collaborate with talented engineers to turn research into reliable, production-ready solutions that improve performance in real-world environments. Openings are available at experienced, senior, and staff levels, with opportunities for technical leadership and mentorship.

Develop, implement, validate, and improve machine learning algorithms for weld perception, including point cloud registration, seam detection, and joint geometry estimation.
Expand perception capabilities across different weld joint types, part geometries, and industrial applications.
Build and maintain data pipelines for model training and evaluation, including annotated 3D scan ingestion, synthetic data generation, and structured dataset management.
Conduct rigorous model evaluation experiments, analyze failure modes, characterize false-positive and false-negative rates, and benchmark performance against quantitative accuracy targets.
Integrate trained models into production ROS-based robotics services, ensuring low-latency inference and compatibility with deployed robotic configurations.
Research and develop advanced perception algorithms using structured-light, RGB, and stereo sensors, including real-time in-process tracking and during-weld seam tracking.
Design and lead experiments involving advanced deep learning architectures, such as transformer-based models and geometric feature learning approaches.
Develop real-time perception systems using sensor fusion and probabilistic state estimation techniques, including Kalman filtering.
Own the end-to-end machine learning lifecycle, from dataset design and annotation strategies to model training, benchmarking, and deployment across robotic fleets.
Establish evaluation frameworks and go/no-go criteria, while improving data acquisition, annotation tooling, distributed training, and hyperparameter optimization workflows.
Assess the balance between synthetic and real-world scan data to improve model robustness and performance.
Provide technical leadership and mentorship to engineers, contribute to code and experiment reviews, and maintain clear documentation of methods, parameters, and results.

Requirements

~2 min read

Master's or Ph.D. in Computer Science, Robotics, Computer Vision, Machine Learning, or a related technical field. Candidates with a bachelor's degree and strong production machine learning experience will also be considered.
At least 3 years of experience for experienced-level openings, or 7+ years for senior and staff-level positions, working in real-world robotics or industrial machine learning.
Strong Python programming skills and hands-on experience with PyTorch, including training, evaluating, and deploying deep learning models in production environments.
Practical experience working with 3D point cloud data and libraries such as Open3D.
Understanding of geometric processing concepts, including surface segmentation, spatial queries, and point-wise labeling.
Familiarity with 3D deep learning architectures such as PointNet++, GeoTransformer, or comparable transformer-based and graph-based approaches for geometric data.
Experience integrating machine learning models into production robotics services, particularly within ROS-based architectures and containerized deployment environments.
Ability to design reliable experiments, evaluate model performance, investigate failure cases, and communicate technical findings clearly.
For senior and staff-level roles: demonstrated experience leading end-to-end machine learning projects from dataset design through fleet deployment, establishing rigorous evaluation and go/no-go frameworks.
For senior and staff-level roles: experience architecting distributed training systems and hyperparameter optimization workflows.
Strong problem-solving, collaboration, and communication skills, with the ability to work effectively across multidisciplinary engineering teams.
Motivation to tackle complex technical challenges and translate advanced machine learning research into practical industrial applications.