Data Engineer

Apple’s Data Org is looking for a Data Engineer to support Apple Ads, where privacy-focused data engineering and machine learning systems power advertising capabilities across real-time, near-real-time, and batch workflows. In this onsite role in Cupertino, you will design and deliver scalable data and ML components, improve reliability and performance, and use AI tools to move faster while validating correctness, privacy, and cost.

This position is based in Cupertino, CA (onsite), with a yearly salary range of USD 129,300 - 194,700. You will work within sprint-based Agile teams alongside engineers and product managers to build reusable, efficient infrastructure for mission-critical systems.

Responsibilities

• Engineer secure, scalable data and machine learning systems across real-time, near-real-time, and batch execution using tools such as Spark, Kafka, and Iceberg, and related technologies
• Own design and delivery of core components, spanning pipeline architecture and ML model development, training, and deployment, including privacy-preserving infrastructure
• Improve reliability, performance, and efficiency, including supporting schema changes, backfills, and experimentation and testing infrastructure such as A/B testing
• Apply a strong understanding of the intersection between business, analytics, and engineering, with an emphasis on reusable and efficient solutions
• Use LLMs and AI coding agents (for example, Claude and Gemini) daily for implementation, testing, and debugging, while continually validating results for correctness, privacy, and cost
• Collaborate with a team of engineers and product managers, growing through code and design reviews and mentoring others as seniority increases
• Contribute to on-call, monitoring, and ongoing reliability and efficiency improvements, with more senior engineers supporting incident response and root-cause analysis
• Operate effectively in a rapidly changing, sprint-based Agile environment, contributing to a culture focused on reliability, resiliency, extensibility, scalability, and productivity

Requirements

• 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, including Spark, Kafka, or 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 (for example, Claude and Gemini) in daily engineering work for tasks such as code generation, review, debugging, test writing, agentic loops, and evaluation systems, to improve skills and velocity
• Excellent collaborative skills
• BS/MS in Computer Science, Software Engineering, Distributed Systems, or a related field

Technologies

• Spark
• Kafka
• Iceberg
• Flink
• Rust
• Python
• Java
• Scala
• LLMs
• Claude
• Gemini
• A/B testing

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Discretionary restricted stock unit awards
• Purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan
• Discretionary bonuses or commission payments (as applicable)
• Relocation (as applicable)

Preferred Qualifications

• 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 (a huge plus)