About this opportunity
Berkshire Grey, Inc. lists this Senior ML Engineer, Computer Vision opportunity in bedford, Massachusetts. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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 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.
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-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 estimation, or anomaly/outlier detection
Demonstrated proficiency to solve real world computer vision problems with machine learning
Demonstrated ability to: Develop on and troubleshoot real robotic systems
Determine and communicate justification of technical priorities
Rapidly prototype and iterate on solutions to challenging problems
Work independently on a variety of projects while maintaining focus
Work in a fast-paced environment with changing priorities
Mentor junior engineers
Strong verbal and written communication skills, capable of explaining complex ideas clearly and concisely to both technical and non-technical stakeholders
Provide technical leadership on key projects
Preferred 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 clutter
Experience with: Robotic vision sensors and camera to robot calibration
Both RGB and depth data
Collecting and training on real and synthetic datasets, including various forms of data augmentation
Applying machine learning to hardware interacting with the real world
Real-time perception-based control
Robot simulators (e.g. Isaac Sim)
Combining model-based and data-driven approaches
Docker, cloud computing, or similar applications
Experiment tracking and dataset management (e.g. Weights & Biases)
Database systems as data source, such as MongoDB
Parallel/distributed systems and asynchronous/concurrent programming
ROS or ROS2
Possessing some of these preferred qualifications is great to have but not required and should not discourage applicants from applying.
RS
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Worksite address
bedford, MA, 01730, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.