Software Engineer

Who you are

• 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
• 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
• 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

What the job involves

• 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
• 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