The Mill (themill.com)

AI Engineer, Computer Vision

San Bruno, California · Senior

Sponsorship not specified$240k-$280kDetected 75 days ago

PythonAWSGCPMachine LearningDeep LearningPyTorchData EngineeringComputer VisionLLMsAgentic AIMLOpsProcurementEmbedded SystemsCommunication

About the role

We're hiring an AI Engineer to work on the AI core of 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 and onboard compute directly into our high-capacity food recycler
models running on the edge identify, classify, and quantify food scraps at the point of generation, and our vision pipeline turns that signal into procurement and operational guidance for large food service operators.

Build and manage the end-to-end ML training pipeline: data ingestion from deployed kitchen units, ground truth generation, annotation tooling (including foundation-model-assisted labeling), training, evaluation, and retraining cycles.
Build the cloud-side evaluation harness that tells us how our shipped edge models are actually performing in the field - automated, reproducible, and aligned to product accuracy targets across food types, kitchen environments, and deployment configurations.
Own MLOps: reproducible training, experiment tracking, model versioning, and automated evaluation against product-defined accuracy targets.
Export and validate models for deployment to edge devices, working closely with the edge team on optimization, quantization, and integration.
Help design and build the LLM- and agent-powered product features that consume waste characterization data and turn it into customer-facing recommendations - purchasing suggestions, anomaly explanations, operational nudges.
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.
Experience building ML training pipelines and data annotation systems at scale.
Experience evaluating ML models rigorously - designing metrics, building the eval harness, and using results to drive product decisions rather than just publish a number.

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.
Proficiency with cloud ML infrastructure (AWS or equivalent) - you've managed training jobs, data pipelines, and experiment workflows in production.
Familiarity with cloud-to-edge model deployment.
Clear, direct communication - you can explain tradeoffs to non-technical stakeholders, push back honestly when you disagree, and write docs that others can follow.

At Mill, it is not typical for an individual to be hired at or near the top of the range for their role.

Python, PyTorch, OpenCV.
Strong familiarity with MLOps on AWS infrastructure.
Experience with LLM and agent frameworks.
Google Cloud / Gemini experience is a plus.
Nice to Have
Experience with video understanding (temporal consistency, tracking, video segmentation)
Experience with foundation models for data annotation
Experience with MLOps tooling (Weights & Biases, MLflow, SageMaker, or equivalents)
Experience shipping LLM- or agent-powered features in a consumer or B2B product
Hardware / IoT product experience, particularly with computer vision and cameras for embedded systems
Mill is a waste prevention technology company reimagining what it means to eliminate waste, starting with food.

The estimated base salary range for this position is $240 to $280k, which does not include the value of benefits or a potential equity grant.

Strong fundamentals in computer vision and deep learning - segmentation, detection, classification, tracking.
Fluency with modern ML approaches - VLMs, LLMs, foundation models, and agentic systems - alongside classical deep learning.

This listing is sourced directly from The Mill (themill.com)'s careers page and normalized into a canonical job model.