B
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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