Computer Vision Intern, Fall/Winter 2026 at Mill

<div class="content-intro"><p><span style="font-weight: 400;">Mill is a waste prevention technology company reimagining what it means 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.</span></p></div><h2><span style="font-weight: 400;">The role</span></h2> <p><span style="font-weight: 400;">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.</span></p> <p><span style="font-weight: 400;">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 </span><strong>San Bruno, CA</strong><span style="font-weight: 400;">.</span></p> <h2><span style="font-weight: 400;">What you'll work on</span></h2> <ul> <li style="font-weight: 400;"><span style="font-weight: 400;">Build and improve computer vision models — detection, segmentation, and classification — for material and food recognition and related tasks</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Curate, explore, and QA large image/video datasets, surfacing failure modes, duplicates, and labeling issues</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">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</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Design and run rigorous model evaluations and contribute to how we measure progress</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Collaborate with engineering and data teams to move promising results toward production</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Document your methods and share findings with the team</span></li> </ul> <h2><span style="font-weight: 400;">What you'll bring</span></h2> <ul> <li style="font-weight: 400;"><span style="font-weight: 400;">Currently pursuing (or recently completed) a </span><strong>Degree</strong><span style="font-weight: 400;"> in Computer Vision, Machine Learning, Robotics or a related field</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Strong programming in </span><strong>Python</strong><span style="font-weight: 400;"> and hands-on experience with a deep-learning framework&nbsp;</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Solid grounding in modern computer vision (object detection, segmentation, representation learning, etc.)</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Experience training and evaluating models on real datasets, and comfort with the messiness of real-world data</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">A genuine interest in translating research skills into practical, shipped systems</span></li> </ul> <p>&nbsp;</p> <h2><span style="font-weight: 400;">Nice to have</span></h2> <ul> <li style="font-weight: 400;"><span style="font-weight: 400;">Experience with MLOps / experiment-tracking tooling (</span><strong>ClearML</strong><span style="font-weight: 400;">, Weights &amp; Biases, MLflow)</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Experience with dataset-management and curation tooling (</span><strong>FiftyOne / Voxel51</strong><span style="font-weight: 400;">, CVAT, Roboflow)</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Familiarity with data/training pipelines, cloud (AWS/GCP), and Docker/Kubernetes</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Publications or open-source contributions in CV/ML</span></li> </ul> <h2><span style="font-weight: 400;">Logistics</span></h2> <ul> <li style="font-weight: 400;"><strong>Location:</strong><span style="font-weight: 400;"> San Bruno, CA — hybrid</span></li> <li style="font-weight: 400;"><strong>Duration:</strong><span style="font-weight: 400;"> </span><em><span style="font-weight: 400;">[e.g., 3–6 months, flexible around your academic schedule]</span></em></li> <li style="font-weight: 400;"><strong>Start date:</strong><span style="font-weight: 400;"> immediate</span></li> </ul> <p>&nbsp;</p> <div><em><span style="font-weight: 400;">The estimated base hourly range for this position is $35 to $45, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs. Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor an employment visa for this role</span></em></div> <div>&nbsp;</div>