Director, Data & AI/ML Platform Engineering

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.

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:

• 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.

• 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 of users and stakeholders. Drive these from problem definition through adoption, not just implementation.
• Compress time from idea to production. Build the developer experience, self-service tooling, and golden paths that reduce friction for every type of user - from engineers and data scientists building pipelines and models, to analysts exploring data in BI tools, to business operators building and running AI-assisted workflows. Speed to insight and speed to production are both critical.
• Lead and grow the organization. Manage engineering managers and senior ICs across three platform areas. Create clarity, remove blockers, and develop people - while continuously evolving how the team works, applying the AI capabilities you build to accelerate your own org's velocity and shaping the skills and structure the team needs for an AI-first engineering model.
• Drive cross-functional alignment. Partner with Data Science, ML Engineering, Data Engineering, Product, and Business leaders to align platform investment with business priorities. Represent the platform in quarterly planning, architecture reviews, and executive forums.
• Communicate with authority at every level. Write crisp strategy documents. Present platform trade-offs to the C-suite. Sit with an engineer and whiteboard a system design. Fluency across these modes is a requirement, not a nice-to-have.
• Run the business. Own budget, headcount planning, vendor relationships, contractor management, and the long-horizon platform strategy. Balance investment in new capabilities with operational excellence and the reduction of legacy 

About You

Requirements:

• Experience:
• 10+ years in software, data, or ML/AI platform engineering; 5+ years leading engineering managers or multi-team platform organizations
• Track record of owning and evolving production-grade platform systems at scale - not just building them, but driving adoption, rationalizing legacy, and measurably improving developer and data science productivity over time
• History of making and landing consequential architectural decisions in complex, high-availability environments; comfort with the full lifecycle from design through post-launch iteration
• Technical Depth:
• Data infrastructure: hands-on experience with distributed compute and storage (Spark, Trino/Presto, Apache Iceberg or Delta Lake), event streaming (Kafka, Flink), workflow orchestration (Airflow), and data governance and quality systems
• ML lifecycle: feature engineering and feature stores, model training pipelines, model deployment and serving (Ray Serve, Triton, or equivalent), monitoring and validation, and the operational practices of running ML in production (MLOps)
• Generative AI and LLMOps: LLM orchestration frameworks, retrieval-augmented generation (RAG), agent architectures, evaluation frameworks, cost and latency governance, and the emerging standards around agentic AI (Model Context Protocol or equivalent)
• Developer platforms: experience building internal developer platforms (IDPs), self-service tooling, and platform abstractions that reduce friction for engineering teams; familiarity with developer experience metrics and platform adoption patterns
• Cloud-native architecture: distributed systems design, container orchestration (Kubernetes), and cloud infrastructure at scale (AWS preferred)
• Platform Product Leadership:
• Product-led mindset. You approach internal platforms the same way a strong product leader approaches external products: segmented user personas, defined success metrics, a prioritized roadmap, and a bias toward adoption and impact over feature completeness.
• 360-degree execution. You own the full loop - discovery and planning, iterative delivery, production quality, user enablement and evangelism, and the feedback loops that close on real-world impact.
• Strategic communication and influence. You can make a compelling case for a multi-year platform investment to a CxO, write a technical design doc your engineers will actually follow, and give a data scientist a useful answer about why their job is slower than it should be. Each of these is a different skill; you have all three.
• User advocacy. You represent users' needs inside the platform team. You hold the bar on developer experience, self-service reliability, and documentation quality. You treat user complaints as signal, not noise.

• We are a group of bright, kind people who are motivated by challenge. We value integrity, innovation and trust. You’ll bring these characteristics to life in everything you do at Stitch Fix.
• We cultivate a community of diverse perspectives— all voices are heard and valued.
• We are an innovative company and leverage our strengths in fashion and tech to disrupt the future of retail. 
• We win as a team, commit to our work, and celebrate grit together because we value strong relationships.
• We boldly create the future while keeping equity and sustainability at the center of all that we do. 
• We are the owners of our work and are energized by solving problems through a growth mindset lens. We think broadly and creatively through every situation to create meaningful impact.
• We offer comprehensive compensation packages and inclusive health and wellness benefits.