Sr. Staff / Distinguished Engineer

Imagine the web itself as your raw material — every page, every image, every signal of what’s new and what matters, flowing in at a scale most engineers only read about in systems papers.

At Apple, that scale is a daily reality. The data acquisition platform you’d help lead doesn’t just power search — it’s the sensory layer underneath Siri, Spotlight, Apple Maps, and the foundation models driving Apple Intelligence. Every one of those products depends on a continuous, exabyte-scale pipeline of fresh, high-quality information pulled from the live web, stored, and made usable in near real time.

As a Sr. Staff / Distinguished Engineer on this team, you won’t just build the pipes — you’ll shape the intelligence that decides what flows through them. This role sits at the intersection of distributed systems and machine learning: the hard problem isn’t only moving exabytes of data, it’s deciding what’s worth acquiring, how to score and prioritize it, and how to continuously improve that judgment as the web and Apple’s models evolve.

You’ll work across engineering, AI research, and product teams to turn crawl and storage infrastructure into a system that gets smarter over time — one whose decisions directly shape the quality of the intelligence millions of people rely on through Apple’s products.

If you’re energized by distributed systems at their most extreme scale, know how to bring ML into the decisions that govern what data gets captured and how it’s valued, and want your judgment to shape the strategic direction of Apple’s AI data foundation, we want to talk to you.

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