Senior Software Engineer, Apple's Streaming Platform and Solutions

Apple•Seattle, WA

About The Position

Apple is seeking an experienced software engineer to join the Data Solutions team within Data Services, responsible for building and evolving the distributed data systems that power Apple's most critical consumer services - including Apple Music, TV, and Podcasts. You'll work on high-scale streaming and data processing platforms, solving hard problems around cross-datacenter replication and getting user data to the edge as fast as possible. Your work will directly impact hundreds of millions of Apple users and the teams that build experiences for them.

Requirements

5+ years of professional experience in server-side Java development with strong understanding of concurrency, memory management, and performance.
Experience designing, building, and operating large-scale distributed systems.
Solid understanding of data structures, algorithms, fault tolerance, and system performance.
Experience with RESTful API design and service-oriented architectures.
Bachelor's degree in Computer Science or equivalent practical experience.

Nice To Haves

Experience with distributed data systems such as Cassandra, Redis, Kafka, or similar platforms.
Experience with Apache Kafka - including broker internals, producers/consumers, and ecosystem tooling - is a strong plus.
Experience with Schema Registry, Object store
Experience with multi-datacenter deployments, replication strategies, and consistency models.
Hands-on experience with cloud platforms and container orchestration (e.g. Kubernetes, AWS, GCP, or similar).
Exposure to observability practices including monitoring, alerting, and performance benchmarking.
Experience with fault injection, chaos engineering, or property-based testing methodologies.
Contributions to open-source projects, especially in the data infrastructure ecosystem.

Responsibilities

Building and evolving distributed data systems.
Working on high-scale streaming and data processing platforms.
Solving problems around cross-datacenter replication.
Getting user data to the edge as fast as possible.
Building and operating data infrastructure that is reliable, scalable, and low-latency.
Focusing on real-time data streaming, large-scale data processing, and optimizing cross-datacenter replication.
Automating existing systems and integrating them into a centralized cloud platform.
Modernizing the infrastructure that underpins these services.
Building the tooling that lets us operate them at scale.
Owning platforms end-to-end: from internals and protocol-level work to operational tooling, observability, and multi-region deployment.