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# Staff / Sr. ML Engineer, Indexing Infrastructure

- **Company:** Apple Inc.
- **Location:** Apple Valley, CA, United States
- **Experience:** Expert
- **Salary:** $216,200
- **Contract:** Permanent contract
- **Skills:** Query Performance, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, BigTable, Cloud Engineering, Databases, Extract Transform Load (ETL), Data Structures, Distributed Systems, Fault Tolerance, MapReduce, Apache HBase, Machine Learning, Metadata, Workflow Management Systems, Parquet, Data Processing, Apache Spark, Multi-Cloud, Indexer, Build Management, Kubernetes, Information Technology, Cassandra, Integration Frameworks, Software Coding
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25196_989bdad77985124d021e8dcfa9442eee

## About the Role

* 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

## Description

* 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.