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.

You'll join a small AI team, building the data and training pipeline that produces our edge CV models, designing the cloud-side evaluation that tells us whether those models are good enough to ship, and helping build the agentic, LLM-driven product features that turn raw waste data into customer-facing insights and recommendations. This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation model, prompting a VLM, and wiring an agent into a product feature.

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 (including foundation-model-assisted labeling), 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

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

About the Role

We're hiring a Senior Engineering Manager to lead Data Integrations at Mill. This role sits at the center of our B2B strategy: you'll own the systems that integrate Mill into our customers' enterprise environments — ERP systems, business applications, internal tools — so that we can combine our food waste estimates with their internal data and use AI to deliver actionable recommendations on how they can reduce waste.

You'll be hands-on technically — designing multi-tenant APIs, building integration pipelines, writing code — while also engaging directly with customers, driving technical partnerships, and managing a team of engineers. You'll use LLMs to accelerate integration development and unlock insights from customer data. This is a senior leadership role that demands genuine technical depth, hands-on ownership, and direct customer engagement.

What You'll Own

Customer System Integration. Design and build the integration layer between Mill and our B2B customers' enterprise environments — connecting to their ERP systems, business applications, and internal tools to pull operational data — purchasing, inventory, consumption patterns, and more. You'll combine that data with Mill's food waste estimates to power AI-driven recommendations that help customers reduce waste. You'll need to deeply understand how enterprise customers operate and what their systems require.

Multi-Tenant API Platform. Architect and operate enterprise-grade APIs for data exchange with customers — including RBAC, tenant isolation, webhooks, and reverse ETL pipelines. You'll own reliability, SLAs, and the developer experience for our partners.

LLM-Accelerated Development. Use large language models to accelerate how we build integrations, automate data mapping across disparate customer systems, and extract actionable insights from customer data. This is a core part of the role, not a side project.

Data Contracts & Governance. Own schema governance, API versioning, and data contracts across integration touchpoints. As we scale B2B relationships, this becomes critical.

Security & Compliance. Ensure data security across the integration layer: tenant isolation, RBAC, encryption, audit logging, and alignment with SOC 2, GDPR, and CCPA.

Customer Onboarding & Technical Partnerships. Be the senior technical face for B2B customers. Scope integration requirements, define data contracts, manage delivery timelines, and build long-term trust.

What We're Looking For

• 10+ years in data engineering, platform engineering, or integration-heavy backend roles, with 3+ years managing engineers
• Deep experience integrating with enterprise customer systems — ERP platforms, business applications, and third-party environments
• Strong background in multi-tenant API design, data pipelines, and integration architecture
• Experience integrating with multiple enterprise ERP and business systems (e.g., NetSuite, SAP, Oracle) — either through iPaaS platforms like Boomi/MuleSoft or through custom-built integration layers. You understand the data mapping, normalization, and error handling challenges that come with connecting to many different customer environments
• Hands-on experience using LLMs to accelerate development workflows, automate data mapping, or build intelligence into integration pipelines
• Strong instincts around data contracts, schema governance, versioning, and API design at scale
• Proven ability to work directly with B2B customers — scoping requirements, running onboarding, and managing technical relationships
• Comfortable context-switching between writing code, reviewing architecture, managing people, and talking to customers

Nice to Have

• Experience in food service, hospitality, retail, or other industries with high food waste relevance
• Experience with EDI transaction sets (850/855/856/810) or retail partner data exchange
• Background in hardware + software or IoT companies
• Experience with ML/CV data pipelines
• SOC 2 Type 2 compliance experience
• Familiarity with data orchestration tools (Airflow, dbt, Spark)

The estimated base salary range for this position is $245k to $270k, 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.