Director, Data & AI/ML Platform Engineering

About Stitch Fix, Inc. Stitch Fix (NASDAQ: SFIX) Stitch Fix is redefining retail by combining human creativity with advanced data science and Generative AI. As we build the future of personalized shopping, we’re equally committed to building yours.  We believe in investing in our team as much as our technology. Join us to be a trendsetter in the industry and help us redefine what’s possible for our clients, while we help you reach your full potential.   About the Role At Stitch Fix, data and AI are not supporting functions - they are the product. Every styling recommendation, merchandising decision, inventory bet, and client interaction is shaped by the platforms this role leads. We are looking for a Director of Data & AI/ML Platform Engineering to lead the engineering organization responsible for three interconnected platform areas: the enterprise data platform that ingests, stores, and makes data queryable at scale; the machine learning platform that enables data scientists and engineers to build, train, and serve models in production; and the generative AI platform that provides the runtime, routing, and integration infrastructure for AI agents and LLM-powered applications across the company. This is a product leadership role as much as it is an engineering leadership role. Your users span the full range of the company - from engineers and data scientists building models and AI applications, to analysts and business partners across every function who are running self-serve analytics, investigating data, and building AI-assisted workflows to do their work. Your job is to understand what each of these user groups needs, set a compelling product vision for each platform area, and drive execution all the way through - from roadmap through adoption.  You will make the consequential architectural decisions that shape how the company builds with data and AI for years. You will own the modernization agenda, manage the trade-offs between speed and stability, and communicate both the strategy and the stakes to stakeholders from engineering peers to the executive team. Why this role? The platforms you would lead are not greenfield experiments. They are live production systems at a public company - real complexity, real stakes, and a clear strategic mandate to modernize and extend them. You'll find a strong technical team, meaningful architectural challenges, and a company that has treated data and AI as a competitive differentiator since its founding. Meaningful scale : petabytes of data, thousands of daily pipelines, and a user base ranging from engineers and data scientists to business operators across every function Strategic mandate : the company's top strategic initiative is building the next generation of AI-powered personalization - this team builds the platform it runs on Real ownership : you will make consequential architectural decisions with real consequences, supported by a leadership team that trusts engineers to own their domain   Responsibilities: What you’ll own Data infrastructure at scale . The systems that ingest, store, and make data accessible across the company - petabyte-scale lakehouse, event streaming, workflow orchestration, data governance, and the self-service tools that make this infrastructure usable without platform team involvement at every step. Machine learning platform . The infrastructure that enables data scientists and engineers to build, experiment, and serve models in production at speed - feature stores, training pipelines, distributed model serving, and the MLOps practices that keep production models healthy, observable, and improving. Generative AI platform . The platform that enables teams across the company to build, deploy, and govern AI agents and GenAI-powered applications - runtime and routing infrastructure, self-service agent-building tools, context and retrieval management, observability and evaluation frameworks, and the cost and safety controls that keep AI reliable, governed, and improving in production. The next generation of personalization and decisioning . The foundational platform work behind the company's highest-priority strategic initiatives - partnering with Data Science, Algorithms, and Product to build the next generation of intelligence infrastructure: deeper understanding of clients, products, and style, powered by real-time data, AI reasoning, and systems that continuously improve. What you’ll do Set and own the product vision for each platform area . Treat internal platforms as products. Understand your users, define north star metrics for platform health and adoption, build a roadmap that earns trust, and communicate the vision in a way that rallies engineers and gains stakeholder buy-in. Own platform modernization decisions . Lead strategic architectural shifts - open table format migration, feature store re-foundation, model serving modernization, agentic AI infrastructure buildout - on behalf