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Senior Machine Learning Engineer (Computer Vision)

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Onsite

Senior Machine Learning Engineer (Computer Vision)

Berkshire Grey

Senior Machine Learning Engineer (Computer Vision)

Skills

MongoDBDeep LearningDockerPythonData ScienceNumPypandasPyTorchMachine LearningBambooHR

1 month ago

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Important information


Contract type:

Permanent

Salary:

$130,000 – $200,000 / year

Location:

Bedford, UK

Work mode:

Onsite

Published on:

15 September 2026

What they need


  • 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
  • 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

Benefits

  • Healthcare - Full medical, dental and vision. Life insurance, gym stipends and wellness programs
  • Vacation - We value hard work and we like to have fun. Take time when you need it
  • Recognition - Peer shout outs, achievement celebrations and leadership awards let you know you’ve made a difference
  • Retirement - Stock options and generous 401(k) matching
  • Nutrients - Lunch is delivered daily, a plethora of snacks, gourmet coffee, Soylent, and top-shelf beverages- Provide technical leadership on key projects
  • 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
  • Rapidly prototype and iterate on solutions to challenging problems
  • Work in a fast-paced environment with changing priorities
  • Demonstrated proficiency to solve real world computer vision problems with machine learning
  • Master’s degree in Robotics, Machine Learning, Computer Vision, Computer Science or a closely related field
  • Demonstrated ability to:
  • Experience with major deep learning frameworks such as PyTorch
  • 4+ years of experience in software development with a focus on robotic manipulation or related areas
  • Determine and communicate justification of technical priorities
  • Work independently on a variety of projects while maintaining focus
  • Strong verbal and written communication skills, capable of explaining complex ideas clearly and concisely to both technical and non-technical stakeholders
  • Develop on and troubleshoot real robotic systems
  • Mentor junior engineers
  • Strong development expertise in Python and C++
  • Experience with data science tools & libraries like numpy, pandas, scipy, matplotlib, scikit-learn
  • 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
  • ROS or ROS2
  • Parallel/distributed systems and asynchronous/concurrent programming

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