Job description

At Apple Ads, we are building the next generation of privacy-focused advertising capabilities. As part of the data organization, we work at the cutting edge of data engineering, machine learning, and privacy at Apple's scale. We are constantly developing data products to provide amazing user experiences and to drive value for developers and publishers. As a member of the Data Org, you will: - Engineer secure, scalable data and machine learning systems across real-time, near-real-time, and batch execution contexts using Spark, Kafka, Iceberg, and beyond - Own the design and delivery of core components — from pipeline architecture to ML model development, training, and deployment — including support for privacy-preserving, mission-critical infrastructure - Drive reliability, performance, and efficiency improvements across your systems, including schema changes, backfills, and the experimentation and testing infrastructure (e.g. A/B testing) needed to validate them - Apply a strong understanding of the intersection between business, analytics, and engineering, with a proactive focus on reusable, efficient solutions - Use LLMs and AI coding agents (e.g. Claude, Gemini) daily to accelerate implementation, testing, and debugging — continually validating every result for correctness, privacy, and cost - Collaborate with a team of world-class engineers and product managers; grow through code and design reviews, and mentor others as you gain seniority - Contribute to on-call, monitoring, and continuous reliability and efficiency improvements; more senior engineers help lead incident response and root-cause analysis - Work effectively in a rapidly changing, sprint-based Agile environment, and contribute to a culture that emphasizes reliability, resiliency, extensibility, scalability, and productivity. We are one team, nurturing each other's growth and supporting each other in delivering for our customers and Apple 1+ years of industry experience building scalable data pipelines and machine learning systems, or other distributed software, at scale Strong computer science and software engineering fundamentals Proficiency in modern programming languages such as Rust, Python, Java, or Scala Experience with distributed systems and data processing technologies (e.g. Spark, Kafka, Flink) Experience building and scaling systems on premise and in the cloud Solid understanding of data structures, algorithms, and system design principles Ability to communicate effectively with cross-functional technical and non-technical teams Hands-on experience using LLMs (e.g. Claude, Gemini) in daily engineering work — for code generation, review, debugging, test writing, agentic loops, and evaluation systems— to continually improve software engineering skills and velocity Excellent collaborative skills BS/MS in Computer Science, Software Engineering, Distributed Systems, or a related field Experience with NoSQL datastores (e.g. Cassandra, Keyspaces, ElastiCache) Experience with lakehouse and Iceberg table formats Experience with anomaly detection Experience with A/B experimentation frameworks History of driving reliability, efficiency, or cost improvements, and mentoring other engineers Comfortable working in a rapidly changing environment with ambiguous requirements Prior experience in the advertising industry is a huge plus

Requirements

Must-haves

Engineer secure, scalable data and machine learning systems across real-time, near-real-time, and batch execution contexts using Spark, Kafka, Iceberg, and beyond
Own the design and delivery of core components — from pipeline architecture to ML model development, training, and deployment — including support for privacy-preserving, mission-critical infrastructure
Drive reliability, performance, and efficiency improvements across your systems, including schema changes, backfills, and the experimentation and testing infrastructure (e.g. A/B testing) needed to validate them
Apply a strong understanding of the intersection between business, analytics, and engineering, with a proactive focus on reusable, efficient solutions
Use LLMs and AI coding agents (e.g. Claude, Gemini) daily to accelerate implementation, testing, and debugging — continually validating every result for correctness, privacy, and cost
Collaborate with a team of world-class engineers and product managers; grow through code and design reviews, and mentor others as you gain seniority
Contribute to on-call, monitoring, and continuous reliability and efficiency improvements; more senior engineers help lead incident response and root-cause analysis
1+ years of industry experience building scalable data pipelines and machine learning systems, or other distributed software, at scale
Strong computer science and software engineering fundamentals
Proficiency in modern programming languages such as Rust, Python, Java, or Scala
Experience with distributed systems and data processing technologies (e.g. Spark, Kafka, Flink)
Experience building and scaling systems on premise and in the cloud
Solid understanding of data structures, algorithms, and system design principles
Ability to communicate effectively with cross-functional technical and non-technical teams
Excellent collaborative skills
BS/MS in Computer Science, Software Engineering, Distributed Systems, or a related field
Experience with NoSQL datastores (e.g. Cassandra, Keyspaces, ElastiCache)
Experience with lakehouse and Iceberg table formats
Experience with anomaly detection
Experience with A/B experimentation frameworks
History of driving reliability, efficiency, or cost improvements, and mentoring other engineers
Comfortable working in a rapidly changing environment with ambiguous requirements
Prior experience in the advertising industry is a huge plus

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaiagilerustpythonjava

Platform: apple
Effort: medium