Computer Vision Engineer

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. About the Role We're hiring a Computer Vision Engineer to work on the CV technology behind Mill Commercial — the computer vision and agentic systems that turn a stream of food waste into operational intelligence for commercial kitchens. Mill Commercial integrates a camera into our high-capacity food recycler; models identify and quantify food scraps, and our pipeline turns that signal into procurement and operational guidance for large food service operators. You'll join a small, capable team, owning the modeling and training infrastructure that powers our CV technology. You will design the cloud-side evaluation harness to determine if edge models meet production targets and build the ground-truth workflows to support them. This is a hands-on IC role for someone who brings deep computer vision fundamentals to fine-tuning models, building MLOps pipelines, and establishing a methodical approach to managing system complexity. What You'll Do Train and evaluate segmentation, classification, and mass-estimation models for the Mill Commercial camera pipeline — from prompting foundation models to fine-tuning ConvNets and VLMs. Optimize edge models for production performance, and operationalize and scale the ML pipeline with model lineage tracking end to end. Create and curate datasets per customer/vertical — more customized, purpose-driven data — to support accuracy targets across food types, kitchen environments, and deployment configurations. Analyze failure cases systematically — unfamiliar food classes, novel kitchen environments, challenging lighting and clutter conditions — and drive the data and modeling decisions that close accuracy gaps. Build annotation tooling and ground-truth generation workflows, including foundation-model-assisted labeling, to keep pace with model iteration. Bring a methodical approach and strong opinions, backed by experience, to the modeling and evaluation decisions you own — and partner with the team's MLOps and edge engineers on training practices, versioning, and deployment tradeoffs as they come up. What We're Looking For Strong fundamentals in computer vision and deep learning — segmentation, detection, classification, tracking — deep enough to make informed architecture calls. Fluency with modern ML approaches — VLMs, LLMs, foundation models, and agentic systems — alongside classical deep learning. You know when to fine-tune a ConvNet, when to prompt a VLM, and when to wire up an agent, and you understand the practical realities of putting any of them into a product. Experience evaluating ML models rigorously — designing metrics, building eval harnesses, and using results to drive product decisions rather than just publish a number. Product shipping experience — you've taken a model to production and dealt with what comes after (drift, edge cases, latency budgets), not just to a benchmark. Bias for action — you'd rather ship a good-enough experiment and learn from it than wait for the perfect plan. Experience making build-vs-buy or tooling decisions backed by data or a clear rubric, not just instinct — you can show your work on how you got there. Clear, direct communication — you can explain tradeoffs to…