Computer Vision

Who you are

• Currently pursuing (or recently completed) a Degree in Computer Vision, Machine Learning, Robotics or a related field
• Strong programming in Python and hands-on experience with a deep-learning framework
• Solid grounding in modern computer vision (object detection, segmentation, representation learning, etc.)
• Experience training and evaluating models on real datasets, and comfort with the messiness of real-world data
• A genuine interest in translating research skills into practical, shipped systems
• Experience with MLOps / experiment-tracking tooling (ClearML, Weights & Biases, MLflow)
• Experience with dataset-management and curation tooling (FiftyOne / Voxel51, CVAT, Roboflow)
• Familiarity with data/training pipelines, cloud (AWS/GCP), and Docker/Kubernetes
• Publications or open-source contributions in CV/ML

What the job involves

• We're looking for a candidate who wants hands-on experience applying their research inside a fast-moving company
• As a Computer Vision Intern, you'll work directly with Mill's Computer Vision team on real production problems — from dataset curation and model training to the pipelines and experiment tooling that make our CV systems reproducible and fast to iterate on
• You'll be mentored by senior members of our Computer Vision team and see your work reach real devices and users
• Build and improve computer vision models — detection, segmentation, and classification — for material and food recognition and related tasks
• Curate, explore, and QA large image/video datasets, surfacing failure modes, duplicates, and labeling issues
• Develop and scale training pipelines and experiment-tracking/reporting so experiments are reproducible and results are easy to compare — i.e., help build the "experiment harness" the team relies on
• Design and run rigorous model evaluations and contribute to how we measure progress
• Collaborate with engineering and data teams to move promising results toward production
• Document your methods and share findings with the team
• Duration: [e.g., 3–6 months, flexible around your academic schedule]
• Start date: immediate