Senior ML Engineer, Computer Vision

About The Job:Berkshire Grey is a leader in the field of AI and robotics, providing innovative solutions for e-commerce, retail replenishment, and logistics. Our technology automates complex pick, pack, sort, and unload operations.As part of the Scoop engineering team, you will develop new approaches to solving challenging trailer-unload computer vision problems for real-world robotic systems. Your work will help robots better perceive, reason about, and interact with dynamic trailer environments, including varied package types, shifting walls, confined spaces, and complex unload conditions. These efforts will enhance the performance, reliability, and throughput of our robotic unloading solutions while unlocking new value for customers. This position offers a unique opportunity to work at the cutting edge of robotics applied to one of the most demanding real-world logistics challenges.Responsibilities:Develop solution prototypes for computer vision problems, to improve our robots’ ability to solve increasingly complex tasks at unprecedented speedsQuickly prototype solutions, creating demos for stakeholders and visitorsServe as subject matter for transitioning prototypes to product teamsIdentify high impact areas for improvements of our robotic systems to solve real problemsStay abreast of the latest advancements in robotics and related fields, evaluating applicability to our challengesAssist with mentorship of more junior engineers or internsCommunicate technical priorities and status.Minimum Qualifications:Master’s degree in Robotics, Machine Learning, Computer Vision, Computer Science or a closely related field.4+ years of experience in software development with a focus on robotic manipulation or related areas.Strong development expertise in Python and C++.Experience with major deep learning frameworks such as PyTorch.Experience with data science tools & libraries like numpy, pandas, scipy, matplotlib, scikit-learnDemonstrated experience training and adapting of existing machine learning architectures for computer vision, such as CNNs/ViTs or VLMs, to solve tasks such as grasp estimation, object detection/segmentation, depth estimation, or anomaly/outlier detectionDemonstrated proficiency to solve real world computer vision problems with machine learningDemonstrated ability to:Develop on and troubleshoot real robotic systemsDetermine and communicate justification of technical prioritiesRapidly prototype and iterate on solutions to challenging problemsWork independently on a variety of projects while maintaining focusWork in a fast-paced environment with changing prioritiesMentor junior engineersStrong verbal and written communication skills, capable of explaining complex ideas clearly and concisely to both technical and non-technical stakeholdersProvide technical leadership on key projectsPreferred Qualifications:MS / PhD in Machine Learning, Computer Vision, Computer Science or a closely related field.Demonstrated technical proficiency in computer vision applied to robotic manipulation, such as grasp estimation or long-tailed object detection & segmentation in clutterExperience with:Robotic vision sensors and camera to robot calibrationBoth RGB and depth dataCollecting and training on real and synthetic datasets, including various forms of data augmentationApplying machine learning to hardware interacting with the real worldReal-time perception-based controlRobot simulators (e.g. Isaac Sim)Combining model-based and data-driven approachesDocker, cloud computing, or similar applicationsExperiment tracking and dataset management (e.g. Weights & Biases)Database systems as data source, such as MongoDBParallel/distributed systems and asynchronous/concurrent programmingROS or ROS2Possessing some of these preferred qualifications is great to have but not required and should not discourage applicants from applying.6110-2607RSDepartmentSoftwareEmployment TypeFulltime-RegularMinimum ExperienceExperienced