Job description

This role owns technical strategy and execution for Apple’s web-scale data acquisition platform — the systems that crawl, store, and deliver exabytes of web content and images to power Siri, Spotlight, Apple Maps, and Apple’s foundation models. Responsibilities span the full lifecycle of acquired data: crawl strategy and prioritization, the ML-driven decisions about what’s worth capturing and how to value it, and the scalable storage and compute architecture that makes all of it usable downstream. As a senior member of the technical staff, you will set direction on how Apple acquires data efficiently, broadly, and precisely at a scale that few infrastructure problems in the industry match. The hardest problems here live at the boundary between distributed systems and machine learning: designing storage and compute that let engineering teams move fast without being bottlenecked by data gravity, while applying ML to decide what to crawl, how to score quality and relevance, and how those decisions should evolve as the web and Apple’s models change. You’ll also shape how downstream teams consume what’s acquired, to maximize its value for search ranking and AI model training alike. Success in this role depends on working across organizational boundaries — partnering with crawl infrastructure, search indexing, and AI/foundation model teams to align architecture and ML strategy with Apple’s broader AI direction. You’ll be expected to navigate ambiguous, large-scale technical tradeoffs independently, and to act as a trusted technical voice to senior leadership on direction, risk, and resourcing for one of Apple’s most foundational data platforms. 12+ years building and operating distributed systems at large scale (petabyte/exabyte-class storage, compute, or data pipelines) Demonstrated experience applying machine learning to data quality, ranking, relevance, or prioritization problems in production Track record of setting technical direction across multiple teams as a senior lead — not just within a single team’s codebase Experience partnering with ML/AI research teams to translate model needs into infrastructure and data requirements B.S. in Computer Science, M.S./PhD Preferred Experience with web crawling, search indexing, or large-scale content pipelines specifically Familiarity with how training data pipelines feed large language or foundation models History of mentoring senior engineers and influencing org-level architecture decisions

Requirements

Must-haves

12+ years building and operating distributed systems at large scale (petabyte/exabyte-class storage, compute, or data pipelines)
Demonstrated experience applying machine learning to data quality, ranking, relevance, or prioritization problems in production
Track record of setting technical direction across multiple teams as a senior lead — not just within a single team’s codebase
Experience partnering with ML/AI research teams to translate model needs into infrastructure and data requirements
Experience with web crawling, search indexing, or large-scale content pipelines specifically
Familiarity with how training data pipelines feed large language or foundation models
History of mentoring senior engineers and influencing org-level architecture decisions

Nice-to-haves

B.S. in Computer Science, M.S./PhD Preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningai

Platform: apple
Effort: medium