Software Engineer, Apple Ads

Apple•Austin, TX

About The Position

Join Apple Ads Engineering to build an advertising platform serving the App Store, Apple News, Stocks, and Apple TV. The team develops technology that helps people discover content, supports creators and publishers, and connects advertisers with audiences while protecting user privacy. Design, develop, launch, and operate internet-scale backend systems for real-time ad selection and delivery. The work includes architecting low-latency, highly available, high-throughput services in a cloud environment; building secure, scalable data-processing pipelines for petabyte-scale datasets; and implementing performant services that handle very high data volumes and queries per second. The role focuses on candidate retrieval, with opportunities to contribute to other backend systems and apply algorithmic search, machine learning, natural language processing, and AI to retrieval challenges. Collaborate with engineering, product, infrastructure, indexing, search, serving, operations, SRE, and program management partners to define requirements and deliver production systems. Write clean, efficient, well-documented code, participate in code reviews, mentor engineers, and debug complex distributed-system issues. The role calls for someone who values privacy, adapts to changing priorities, and works effectively both independently and with a team.

Requirements

At least 3 years of industry experience architecting and developing internet-scale server software for Linux, including scalable microservices or data-driven platforms.
Strong grounding in computer science fundamentals, including algorithms and data structures.
Strong coding skills in Go, Java, Rust, C++, Scala, or a similar object-oriented or functional language.
Hands-on experience designing and operating scalable microservices.
Experience with search technologies such as Lucene, Solr, or Elasticsearch.
Expertise with NoSQL datastores such as Cassandra.
Proficiency with data-processing technologies such as Kafka, Spark, or Flink, and methods for processing petabyte-scale datasets.
Experience with AWS services such as Amazon S3, EC2, and EKS/Kubernetes.
Strong problem-solving and debugging skills.
Ability to work through complex problems and systems, independently or with a team, and deliver complete systems end to end.
Excellent written and verbal communication skills and ability to work with technical and non-technical cross-functional partners, including operations and project management.
Bachelor's degree in Computer Science or equivalent experience.

Nice To Haves

Experience creating technology for algorithmic search, machine learning, natural language processing, and artificial intelligence.
Experience building ad-serving, recommendation, or large-scale retrieval systems.
Familiarity with real-time bidding, candidate retrieval, or ranking pipelines.
Experience operating services with strict latency and availability SLOs.
Familiarity with privacy-preserving system design and data-handling practices.
Strong understanding of distributed-systems fundamentals, including consensus, replication, and fault tolerance.
Motivation to learn new technologies and thrive in a fast-paced, challenging environment.

Responsibilities

Architect, design, and implement internet-scale, low-latency, highly available ad-serving systems in a cloud environment.
Build performant microservices and distributed data platforms that support high data volumes and queries per second.
Engineer secure, scalable distributed data-processing pipelines for petabyte-scale indexing and retrieval datasets.
Apply algorithmic search, machine learning, natural language processing, and AI techniques to candidate retrieval and ranking.
Collaborate with internal teams to support indexing and search use cases across the organization.
Partner with product managers, operations, SREs, and engineers to define requirements and deliver end-to-end systems.
Deliver complete systems from design through production, both independently and as part of a team.
Write clean, efficient, well-documented code and participate in code reviews.
Debug and resolve complex issues across large-scale distributed systems.
Stay current with advances in search, retrieval, distributed systems, ML/AI, and cloud infrastructure, and contribute ideas to improve the platform.
Mentor other engineers on the team.