Senior Software Engineer, Apple Ads

Apple•Cupertino, CA

11h

About The Position

Join Apple Ads Engineering to build an advertising platform for the App Store, Apple News, Stocks, and Apple TV. The platform helps people discover content, supports creators and publishers, and connects advertisers with audiences while maintaining Apple’s commitment to privacy. Design, develop, launch, and operate internet-scale backend systems for real-time ad selection and delivery. The role focuses on candidate retrieval and includes building low-latency, highly available, high-throughput ad-serving systems in a cloud environment, along with secure, scalable data-processing pipelines for petabyte-scale datasets. You will collaborate across engineering, product, infrastructure, indexing, search, serving, operations, and program management to deliver production systems. There may also be opportunities to apply algorithmic search, machine learning, NLP, and AI to retrieval problems and contribute across other backend systems. The role involves writing clean, efficient, well-documented code; participating in code reviews; debugging large-scale distributed systems; and mentoring engineers. It calls for someone who enjoys building privacy-protecting products and can work effectively in a fast-paced environment with changing priorities.

Requirements

At least 6 years of industry experience architecting and developing internet-scale server software for Linux, including scalable microservices or data-driven platforms.
Strong 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 on complex problems and systems, independently or collaboratively, and deliver complete end-to-end systems.
Excellent written and verbal communication skills, including effective collaboration with technical and non-technical cross-functional teams.
Bachelor’s degree in Computer Science or equivalent experience.

Nice To Haves

Master’s degree in Computer Science or a related field.
Experience creating technology for algorithmic search, machine learning, natural language processing, or 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, implement, and operate 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.
Develop secure, scalable distributed data-processing pipelines for petabyte-scale indexing and retrieval datasets.
Apply algorithmic search, machine learning, natural language processing, and AI to candidate retrieval and ranking.
Collaborate with internal teams on 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, independently and as part of a team.
Write clean, efficient, well-documented code and participate in code reviews.
Debug and resolve complex issues in large-scale distributed systems.
Mentor other engineers and contribute to engineering best practices.
Keep current with advances in search, retrieval, distributed systems, ML/AI, and cloud infrastructure, and contribute platform-improvement ideas.