Software Development Engineer II (Planning and Forecasting)

Quince•Palo Alto, CA

3d•$162,000 - $182,000

About The Position

Quince is building its own supply chain planning platform from scratch. This is the system that will run forecasting, inventory placement, vendor management, and the weekly ordering cadence for a $10B+ business expanding across North America and Europe. As an early engineer on the Planning Tools team, you’ll be hands-on across data pipelines, model-serving infrastructure, integrations, and the operator-facing tools that put science in the hands of the planning team. This is the team that builds, not maintains. You’ll work alongside a group of senior engineers and data scientists shipping the first production cuts of every component driving the forecasting and planning roadmap. Your work will be used by the people running Quince’s supply chain every week, and the gap between what you ship and the impact it has will be unusually short. We expect you to ship code AI-natively. AI-augmented coding, test generation, and exploratory data work are how this team moves; fluency with these tools is part of the bar, not a bonus.

Requirements

2 to 4 years of professional software engineering experience shipping production systems.
Strong fundamentals: data structures, algorithms, debugging, and clean code. You can hold your own in a code review with senior engineers.
Comfortable across the stack: data pipelines, APIs, and the basics of model-serving or ML infrastructure.
Demonstrable AI-native engineering practice: regular use of AI coding assistants, AI-augmented testing, and AI-augmented data exploration. You should be able to talk specifically about how AI changed how you build.
Comfort with ambiguity and a bias to ask, not assume.
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.

Nice To Haves

The ideal candidate is a strong mid-level engineer with 2-4 years of production experience, growing fast, and energized by ambiguity.
They take a well-shaped task and turn it into shipped, well-tested code without needing the design fully decided up front.
They ask sharp questions, push back when something doesn’t make sense, and leave the codebase a little better than they found it.
They are comfortable working in a small team where the senior people around them expect them to leverage AI coding assistants fluently.
They write clean prompts, review AI-generated code with the same rigor as their own, and know when to step out of the AI loop and write the hard part by hand.

Responsibilities

Implement features end-to-end across data pipelines, model-serving plumbing, integrations with vendor and order management systems, and operator-facing tooling, moving from ticket to production with high craft and minimal hand-holding.
Own the quality of what you ship: typed, tested, observable, and easy for the next engineer to read.
Use AI coding tools as part of your default workflow for scaffolding, refactors, test generation, and exploratory data analysis. Apply high judgment to verify and tighten what they produce.
Take ownership of services and pipelines you’ve built: monitoring them, responding to alerts, and fixing what breaks.
Improve the team’s operational health, including dashboards, runbooks, and alerts, as a routine part of feature work, not a separate project.
Partner closely with the planning team’s operators, data scientists, and other engineers. Ask for the context you need, surface tradeoffs early, and give honest estimates.
Participate in design reviews and architectural discussions; bring evidence and ask questions; learn from the seniors around you.
Contribute to code review and team-level engineering hygiene.