Software Development Engineer III (Planning and Forecasting)

Quince•Palo Alto, CA

4d•$205,000 - $215,000

About The Position

Quince is building its own supply chain planning platform from scratch. As a senior engineer on the Planning Tools team, you’ll lead the build of meaningful slices of that platform: the model-serving infrastructure, the experimentation framework, the operator console, and the integrations that put the planning system at the center of the weekly ordering cadence. You’ll partner directly with the Principal Engineer setting the platform architecture and with the Science team owning the science roadmap, taking their direction and turning it into shipped, reliable systems. You’ll be senior enough to make the local technical calls that don’t need to escalate, and to mentor the engineers around you. And you’ll be in a small enough team that your work will visibly compound. We expect AI-native engineering throughout. Not just personally fluent with AI coding tools, we expect you’ll be one of the people on this team setting the standard for how AI shows up in our development workflow, our test strategy, our observability, and the operator-facing capabilities we ship. The ideal candidate has 5-8 years of production engineering experience, with real ownership of non-trivial systems behind them. They’ve led workstreams through scoping, designing, and shipping with a small group around them, and mentored junior engineers along the way. They’re technically opinionated but collaborative; they argue for what they think is right and accept being shown a better answer. They’ve built data-intensive or ML-adjacent systems before - not necessarily as the data scientist in the room, but as the engineer who knows enough to make models run reliably in production. And they have lived with the consequences of their architecture choices in operation, not just in design. They are AI-native by reflex. They use AI tools across their development workflow, hold the line on quality of AI-generated output, and are visibly experimenting with where AI shows up in product capabilities like LLM-augmented operator interfaces, AI-assisted incident triage, agentic workflows on top of forecasting and planning data.

Requirements

5-8 years of software engineering experience, with demonstrated ownership of production systems beyond features-on-rails
Track record of leading workstreams end-to-end with a small team around you
Strong fundamentals across data systems, APIs, pipeline orchestration, and production operations; comfort with ML-adjacent infrastructure (feature pipelines, model serving, evaluation harnesses)
AI-native engineering at a level where you can credibly set standards: specific examples of how AI tools changed your shipping cadence, your testing strategy, or the products you’ve built
Mentorship track record with adjacent engineers who became better because they worked with you
Bachelor’s degree in Computer Science, Engineering, or equivalent depth of professional experience

Responsibilities

Own a meaningful piece of the planning platform (e.g., feature pipelines, model serving, the experimentation framework, the operator console, or a critical integration) from design through production.
Make sound technical tradeoffs within your area without escalating every call; collaborate with leadership as needed
Drive the engineering quality bar within your workstream: testing, observability, performance, and operability
Use AI coding tools fluently in your day-to-day work and help raise the bar across the team for how we build with them
Identify AI-augmented capabilities to ship into the platform itself: LLM-aided observability and root-cause analysis, natural-language operator interfaces over planning data, agentic workflows for routine planning tasks
Hold the team’s standards on AI-generated code: review rigorously, refactor when needed, and treat AI assistance as leverage, not a free pass
Mentor juniors on design, code review, and operational ownership; raise the average level of the engineers around you
Drive design reviews for your area; bring rigor to architectural decisions and a willingness to hear pushback
Partner directly with data scientists on what the platform should expose for them, and with the planning team’s operators on what they need to do their job better
Translate fuzzy operational problems into well-shaped engineering work, without waiting for a PM to translate