Principal Engineer - Supply Chain Planning
Quince · United States, Remote
- Location
- United States, Remote
- Funding
- N/A
- Posted
- Aug 20, 2026
Quince is hiring a Principal Engineer - Supply Chain Planning based in United States, Remote. Every apply link on Engg.space goes straight to the company's own careers page - no recruiter middleman, no generic job-board form.
Apply directly at QuinceRole details
ABOUT QUINCE Quince is a destination for builders, creators, innovators, and operators who want to come together and challenge the status quo. Our mission is simple: make really high quality essentials for really low prices, fairly and sustainably. We deliver on that mission through a unique manufacturer-to-consumer (M2C) model eliminating the layers of traditional retail that add cost and result in consumers paying more than they need to. We find, build relationships with, and work directly with the manufacturing partners behind some of the world’s finest products. From there, our teams design smart, efficient operational processes and build and deploy proprietary technology, AI, and analytics to help us scale fast. What began with a small assortment of elevated basics has quickly grown into a cross-category brand spanning apparel, accessories, home goods, and more. Today, tens of millions of people across a growing number of countries come – and return – to Quince because they trust us to deliver. OUR CULTURE Quince is a culture built for builders by builders. Our way of working starts with a blank sheet of paper. We question conventional thinking, use technology and data to uncover new opportunities, and move quickly to turn ideas into reality. We aren’t interested in replicating how others do retail. We’re building a better way – at a speed and scale unlike anything that’s been done before. We dream big and chase the hard problems others shy away from. Rejecting long-held assumptions is part of our company's DNA. Where conventional wisdom says you have to choose – soft or durable, speed or rigor, quality or price – we ask why that trade-off has to exist in the first place. Our pace is fast and the bar is high because our customers expect a lot from us and we refuse to let them down. We believe the best results come from challenging ourselves, learning from one another, and building on each other's strengths. Here, responsibility is not determined by role, tenure, or seniority. Every team member - no matter their level - has the opportunity to drive our business and shape our trajectory. If you’re someone who likes to imagine new possibilities and build better systems rather than plug-in to outdated ones, Quince is the place for you. THE ROLE Principal Engineer - Supply Chain Planning Quince is building its own supply chain planning platform from scratch because the model we operate doesn’t fit anything off the shelf. Our supply chain runs factory-direct, at high frequency, with short lead times, across a growing number of vendors, fulfillment centers, and markets worldwide. The planning and forecasting layer that coordinates all of this is being rebuilt, and as Principal Engineer for Supply Chain Planning Tools, you will build it. This role is about building the production systems that make data science real, specifically the platform infrastructure that takes forecasting models and optimization algorithms from development into the weekly cadence that runs the business. You’ll need to understand the science well enough to partner with the people who do it, translate it into reliable systems, and give operators the controls to work alongside it and override it when needed. This is a 0-to-1 role. You won’t inherit a system, but a set of business workflows. You’ll design and build one, with a small and highly capable team, starting with demand forecasting and planning infrastructure and expanding into logistics and warehouse optimization. The platform we'll build is AI-native by design. AI-augmented operator interfaces, LLM-aided observability, agentic workflows over planning data, and AI-assisted incident triage are first-class capabilities on the roadmap, not afterthoughts. The Principal Engineer will set the bar for both — how the team builds with AI, and how AI shows up in what we ship. The ideal candidate is a seasoned production engineer who has spent meaningful time in the orbit of data science and ML, not as a practitioner, but as the person who makes it work in production. They have built model pipelines, experimentation frameworks, feature infrastructure, and operator tooling that bring algorithmic systems to life at scale. They can walk into a room with supply chain practitioners, understand their problems at a strategic level, and independently determine what to build. They don’t wait for a PM to translate, and they don’t wait for a large team to start executing. They thrive in environments where strategy, innovation, and decision-making are intentionally distributed, where candor, speed, and data are highly valued, and colleagues at all levels hold each other to unusually high standards on behalf of Quince customers. Responsibilities: AI native Platform Architecture & Build Architect and build Quince’s proprietary supply chain planning platform from the ground up to be multi-vendor, multi-modal, multi-market, and built to scale. Design and own the full model pipeline lifecycle, including feature engineering, forecasting tournament framework, evaluation, deployment, monitoring, and refresh, and the experimentation framework that lets scientists iterate safely in production. Build integrations with vendor management, order management, and inventory platforms so the planning system sits at the center of the weekly ordering cadence Set the standard for how the team builds with AI — coding assistants, AI-generated tests, AI-augmented data exploration — and hold the line on quality of AI-generated output through review rigor and refactoring. Architect AI-augmented capabilities directly into the platform: LLM-aided observability and root-cause analysis, natural-language operator interfaces over planning data, agentic workflows for routine planning tasks. Partner with the science team on the infrastructure that AI-driven models need, such as vector stores, prompt versioning, and evaluation harnesses for LLM-based components, so AI-driven science can run reliably in production along