Computer Vision, Fall/Winter 2026

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. This is a hybrid role based in San Bruno, CA.

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

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

Location: San Bruno, CA — hybrid

Duration: [e.g., 3–6 months, flexible around your academic schedule]

Start date: immediate