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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Computer Vision Engineer - **Company:** The Mill - **Location:** San Bruno, CA, United States - **Salary:** $220,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Vision, Python (Programming Language), Machine Learning, OpenCV, Software Tools, Management of Software Versions, Pytorch, Large Language Models, Multi-Agent Systems, Deep Learning, Machine Learning Operations - **Published:** August 16, 2026 - **Apply:** https://www.dice.com/job-detail/e1055ec6-ccac-4339-bcba-a7e6477c9382 ## About the Role * 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 non-technical stakeholders, push back honestly when you disagree, and write docs that others can follow. * Genuine interest in applying AI to food waste reduction and sustainability. This is a mission-driven product and we want people who care about the mission. * Software skills: Python, PyTorch, OpenCV. Experience with LLM and agent frameworks. Nice to Have * Experience with video understanding (temporal consistency, tracking, video segmentation) * Experience with MLOps tooling (Weights & Biases, MLflow, SageMaker, ClearML, or equivalents) * Hardware / IoT product experience, particularly with computer vision and cameras for embedded systems ## Description 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. ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Robots 2.0: When artificial intelligence meets steel](https://www.wearedevelopers.com/videos/1452-robots-2-0-when-artificial-intelligence-meets-steel) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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