We’re looking for an ML Engineer to help build and scale the indexing infrastructure powering web and image search. The team runs both petabyte-scale offline batch pipelines and near-real-time indexing systems that turn web documents, images, and metadata into production-ready indexes.
We work across distributed computing, data processing, and storage, while partnering with crawling, ranking, ML, retrieval, and infrastructure teams to shape the long-term indexing strategy. This is a cross-functional role requiring strong technical depth, organizational influence, and the ability to drive complex, multi-team initiatives.
You’ll own both technical direction and execution for two core systems: large-scale offline indexing (distributed pipelines processing petabytes of content into production indexes) and fresh/near-real-time indexing (low-latency propagation of new or updated content). Responsibilities span architecture, scalability, reliability, and efficiency across billions of documents and images.
Success requires close collaboration across teams to resolve dependencies, make architecture tradeoffs, and drive large programs to completion, while acting as a key liaison to senior leadership on strategy, risks, and resourcing.
Lead and grow a team building large-scale web and image indexing infrastructure
Own architecture and execution for both offline batch pipelines and near-real-time indexing systems
Drive improvements in freshness, throughput, scalability, reliability, and efficiency
Enable rapid experimentation for search quality and ML teams introducing new signals
Build strong cross-team partnerships (crawling, ranking, ML, storage, serving) and align on architecture and delivery milestones
Represent the indexing org in leadership reviews, communicating strategy, progress, and tradeoffs
Recruit and develop engineers, fostering a culture of ownership and technical excellence
Significant experience building large-scale distributed systems or data infrastructure
Engineering management experience leading software teams
Strong grasp of distributed systems, large-scale data processing, and storage
Experience with distributed batch processing (e.g., MapReduce-style models)
Track record leading multi-team technical initiatives and influencing without direct authority
Strong communication skills for technical topics with senior leadership
BS in CS or related field, or equivalent experience
Experience with batch/streaming frameworks (Spark, Beam, Flink, Kafka, etc.)
Experience with distributed storage systems (Bigtable, HBase, Cassandra)
Background in search, information retrieval, or large-scale content processing
Experience with image processing, computer vision, or multimodal systems
Experience optimizing distributed systems for cost, latency, and throughput
Experience driving cross-org programs and presenting strategy to executive leadership
Track record developing senior engineers into technical leadership roles
Job description provided by Apple.
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