Computer Vision, Fall/Winter 2026

Mill•San Bruno, CA

3d•Hybrid

About The Position

Mill is a waste prevention technology company that is reimagining how to eliminate waste, starting with food. We build smart systems and infrastructure for homes, businesses, and municipalities that transform food scraps from landfill-bound waste into valuable resources, including chicken feed. Tens of thousands of Mill’s residential food recyclers are already helping households divert millions of pounds of food scraps every year, paving the way for our upcoming launch of Mill Commercial—the industry’s first end-to-end solution for managing, understanding, and preventing food waste in commercial environments (e.g. grocery, restaurants, food services). At Mill, we are passionate about building easy-to-use, beautifully designed technologies that keep food in the food system and out of landfills. This is a hybrid role based in San Bruno, CA.

Requirements

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
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor an employment visa for this role

Nice To Haves

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

Responsibilities

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