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)
