---
title: 'Computer Vision Engineer at Mill'
canonical: 'https://feeny.ai/job/computer-vision-engineer-mill-san-bruno-07pckkhsg0nf'
type: 'job'
last_seen: '2026-09-04'
---

# Computer Vision Engineer at Mill

- **Company:** Mill
- **Location:** San Bruno, CA
- **Posted:** 2026-08-12
- **Last confirmed live:** 2026-09-04
- **Apply:** https://job-boards.greenhouse.io/mill/jobs/4723231005

## Job description

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 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

The estimated base salary range for this position is $220-250K, 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. At Mill, it is not typical for an individual to be hired at or near the top of the range for their role.

## About Mill

## Company Overview
- **One-liner**: Mill makes it easy to keep food out of landfills with innovative, odorless food recycling solutions for homes, workplaces, and cities.
- **Entity Type**: Private (Series C)
- **Headquarters**: San Bruno, California, USA
- **Founded**: 2020
- **Founders**: Not publicly available (founded by a team from Nest Labs / Google, though specific names are not widely publicized)

## Core Business
- Primary industry: Environmental Services / Consumer Hardware & Software
- Target customers: B2C (homeowners), B2B (grocery and food service partners via Mill Commercial)
- Mission: To eliminate waste for good, starting with the food that ends up in landfills. The goal is to turn kitchen scraps into food for chickens, keeping food in the food system and out of landfills.

## Products & Services
- **Mill Food Recycler (Home)**: An odorless, effortless food recycling bin for the home. It uses intelligent tech to dehydrate and reduce food volume by 80% overnight. Users do not need to press a button; the device senses how much is added. The resulting grounds are shipped to Mill's small-farm partners for use as chicken feed. Type: Consumer Hardware + Service (subscription).
- **Mill Commercial**: A new solution for grocery stores and food service businesses to manage food waste at scale, leveraging the same core technology. Type: B2B Hardware + Service.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Total Funding**: $170.8M across 6 rounds [linkedin.com](https://www.linkedin.com/company/mill)
- **Key Metric**: 21,000,000+ pounds of food kept out of landfills [mill.com](https://www.mill.com/)
- **Notable Investors/Partners**: Prelude Ventures (led Series C), Climate Pledge Fund (Amazon), and others [linkedin.com](https://www.linkedin.com/company/mill)
- **Growth Signals**: 110 total employees [builtin.com](https://builtin.com/company/mill) (LinkedIn reports 548, likely including contractors/partners) [linkedin.com](https://www.linkedin.com/company/mill); 14 active job postings as of mid-2026; launch of Mill Commercial; headcount growth of +0.4% monthly [linkedin.com](https://www.linkedin.com/company/mill)

## Competitive Advantages
- **Hardware + Software Moat**: The combination of a custom hardware appliance, embedded firmware, and a computer vision AI engine creates a difficult-to-replicate system.
- **Network Effect**: The more users Mill has, the more food waste is diverted, strengthening their supply chain of grounds for chicken feed and their partnerships with farms.
- **Brand & Design**: A strong focus on industrial design and user experience (odorless, effortless, beautiful) differentiates it from compost pails and garbage disposals.
- **Climate Impact**: Directly addresses methane emissions from landfills (methane is 80x more potent than CO2), giving it a strong mission-driven narrative.

## Strategic Focus
- **Scaling the Home Business**: Expanding the subscriber base for the home food recycler.
- **Launching Mill Commercial**: Entering the B2B market with grocery and food service partners to capture waste upstream.
- **International Expansion**: Likely future focus, given the global scale of food waste.

## Why Work Here
- **Mission-Driven**: Employees are directly working on climate change, keeping food out of landfills.
- **Hybrid Work**: Hybrid work environment (typical time on-site: 2 days a week) [builtin.com](https://builtin.com/company/mill).
- **Culture**: Described as a mix of hardware, software, and AI engineering with a startup feel. Offers company equity, 401(K), generous parental leave, free snacks, and company-sponsored family events [builtin.com](https://builtin.com/company/mill).
- **Engineering Focus**: Heavy investment in hardware engineering (San Bruno) and software/AI (Computer Vision). Roles include Hardware Test Engineer, Senior Firmware Engineer, AI Engineer (Computer Vision), and Product Design Engineer [greenhouse.io](https://job-boards.greenhouse.io/Mill).
- **Employee Rating**: 3.4/5.0 on LinkedIn (23 reviews), with Culture rated 3.7 and Compensation rated 3.6 [linkedin.com](https://www.linkedin.com/company/mill).

## Sources
1. [mill.com](https://www.mill.com/)
2. [greenhouse.io](https://job-boards.greenhouse.io/Mill)
3. [builtin.com](https://builtin.com/company/mill)
4. [linkedin.com](https://www.linkedin.com/company/mill)

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