Data Engineer

Build privacy-focused data engineering and machine learning systems for Apple Ads inside Apple’s Data Organization. This onsite role in Cupertino, CA blends real-time, near-real-time, and batch advertising capabilities with scalable engineering, reliability improvements, and hands-on use of LLMs and AI coding agents to accelerate implementation and debugging.

As a Data Engineer, you will design and deliver core data and ML components, strengthen pipeline performance and dependability, and support mission-critical infrastructure that must meet privacy and correctness expectations. You will collaborate with product managers and world-class engineers through code and design reviews, contribute to incident response, and grow your impact over time.

Compensation: USD 129,300 - 194,700 per year. Minimum experience: 1+ years. Education: BS/MS in Computer Science, Software Engineering, Distributed Systems, or a related field.

What you’ll do

• Engineer secure, scalable data and machine learning systems across real-time, near-real-time, and batch execution using Spark, Kafka, Iceberg, and related technologies
• Own end-to-end delivery of core components, from pipeline architecture to ML model development, training, and deployment, including privacy-preserving infrastructure
• Improve reliability, performance, and efficiency through schema changes, backfills, and the experimentation and testing infrastructure (including A/B testing) to validate updates
• Apply a strong understanding of the intersection between business, analytics, and engineering to deliver reusable, efficient solutions
• Use LLMs and AI coding agents (e.g., Claude, Gemini) daily to accelerate coding, testing, and debugging, while continuously validating results for correctness, privacy, and cost
• Contribute in an Agile, sprint-based environment that emphasizes reliability, resiliency, extensibility, scalability, and productivity
• Participate in on-call, monitoring, and ongoing reliability and efficiency improvements, with more senior support for incident response and root-cause analysis
• Collaborate with cross-functional partners and grow through code and design reviews, including mentoring others as you advance

What you bring

• 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 such as Spark, Kafka, Flink
• Experience building and scaling systems on premise and in the cloud
• Solid understanding of data structures, algorithms, and system design
• 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, including code generation, review, debugging, test writing, agentic loops, and evaluation systems to improve software engineering skills and velocity
• Excellent collaboration skills
• BS/MS in Computer Science, Software Engineering, Distributed Systems, or a related field

Technologies you’ll work with

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

Benefits at Apple

• Comprehensive medical and dental coverage
• Retirement benefits
• A range of discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through Apple’s discretionary employee stock programs
• Discretionary restricted stock unit awards
• Ability to purchase Apple stock at a discount through the Employee Stock Purchase Plan if voluntarily participating
• 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
• Comfort with rapidly changing environments and ambiguous requirements
• Prior experience in the advertising industry (huge plus)