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Berkshire Grey

Robot type
Warehouse and Logistics
Location
BedfordMassachusettsUSA
Job type
Artificial Intelligence
Posted
Sep 7, 2026
Salary
$130,000–$200,000 a year
Full-time

Senior ML Engineer, Computer Vision

Job description

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.

Job responsibilities

  • Develop solution prototypes for computer vision problems, to improve our robots’ ability to solve increasingly complex tasks at unprecedented speeds
  • Quickly prototype solutions, creating demos for stakeholders and visitors
  • Serve as subject matter for transitioning prototypes to product teams
  • Identify high impact areas for improvements of our robotic systems to solve real problems
  • Stay abreast of the latest advancements in robotics and related fields, evaluating applicability to our challenges
  • Assist with mentorship of more junior engineers or interns
  • Communicate technical priorities and status.

Job requirements

  • 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-learn
  • Demonstrated 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…
  • Demonstrated proficiency to solve real world computer vision problems with machine learning
  • Demonstrated ability to: Develop on and troubleshoot real robotic systems
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