Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
Do you want to make Siri and Apple products smarter for our users? The MLPT & Infra teams are building groundbreaking technology for algorithmic search, machine learning, natural language processing, and artificial intelligence. Our universal search engine powers search features across a variety of Apple products, including Siri, Spotlight, Safari, Messages and Lookup.
As part of this group, you will build the storage and compute foundation behind one of the most exciting high-performance computing environments: trillions of records, petabytes of data, and the systems that turn them into search and Apple Intelligence experiences that delight our customers every day.
Design and build our web signal store, a petabyte-scale Parquet/Iceberg lakehouse holding trillions of web documents and signals.
Design and build our in-house MapReduce-style processing framework, which runs thousands of workers on Kubernetes.
Improve performance, reliability, and cost at trillion-row scale, from read/write throughput and compaction to fault-tolerant batch jobs on shared clusters.
Own production health, including monitoring, on-call, and root-cause fixes, and raise the engineering bar through design and code reviews.
Strong coding skills and a solid computer science background in algorithms, data structures, and distributed systems
5+ years experience designing, building, and operating large-scale distributed systems such as databases, storage platforms, or data processing frameworks
Hands-on experience with petabyte-scale batch data processing (MapReduce, Spark, or similar)
Proficiency in cloud-native architectures and compute orchestration on AWS (S3, EC2, EKS) and Kubernetes
Excellent interpersonal skills, with the ability to work independently and drive cross-team efforts
MS or PhD in Computer Science or related field
Deep familiarity with columnar and lakehouse formats (Parquet, Iceberg), and the judgment to choose between them at scale
Experience with storage internals: compaction, metadata and manifest management, indexing, and query performance tuning
Experience with workflow orchestration (e.g., Argo) and resource scheduling on shared Kubernetes clusters
Experience with key-value or online serving stores (e.g., Bigtable, HBase, Cassandra) and multi-cloud data movement
Experience with search indexing, machine learning, or NLP is a plus
Job description provided by Apple.
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