LandOfferStaff / Sr. ML Engineer, Indexing Infrastructure
Job details
- Level
- Staff / principal
- Experience
- 5+ years
- Education
- PhD
- Posted
- Oct 29, 2026
- Last confirmed open
- Oct 30, 2026
What you'll need to apply
What this employer's standard application typically asks
Company-specific questions
- Have you previously worked at Apple?
About this role
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.
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