AI Engineer, Computer Vision

About the role

Mill is a waste prevention technology company focused on eliminating food waste. We develop smart systems for homes, businesses, and municipalities to convert food scraps into valuable resources like chicken feed. Our residential food recyclers have diverted millions of pounds of food waste annually, and we are now launching Mill Commercial, an end-to-end solution for commercial kitchens.

What You'll Do

• Build and manage the end-to-end ML training pipeline: data ingestion from deployed kitchen units, ground truth generation, annotation tooling, training, evaluation, and retraining cycles.
• 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.
• 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. Define how agents call tools, ground in customer data, and stay reliable in production.
• 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.

What We're Looking For

• Strong fundamentals in computer vision and deep learning — segmentation, detection, classification, tracking. You understand the architectures well enough to make informed choices.
• 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 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.
• 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.
• 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. 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

Pay

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. 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.