Job Overview:
The AIML – Sr Data Engineer, Evaluation role at Apple involves designing and maintaining large-scale batch and streaming data pipelines to support Siri, Search, and Machine Learning products. You will collaborate with product and data science teams to ensure accurate data collection, validate data flows, and build high-performance data models. The position requires expertise in distributed systems, data modeling, and modern programming languages, while also emphasizing automation, quality standards, and scalability. A strong background in machine learning pipelines and experience supporting ML engineers or data scientists is preferred, along with excellent problem-solving and communication skills.
>> View full job details on Apple’s official website.
Resume and Interview Tips:
To tailor your resume for this senior data engineering role at Apple, focus on highlighting your hands-on experience with distributed data processing technologies like Spark, Flink, and Kafka. Quantify your achievements, such as the scale of data you’ve handled (e.g., petabytes) or improvements in pipeline efficiency. Emphasize your contributions to data quality, automation, and tooling that made data more accessible for analytics or ML. If you’ve worked on machine learning pipelines or supported data scientists, detail those collaborations—this is a key differentiator for Apple. Showcase your proficiency in Python, Java, or Scala, and mention any open-source contributions or relevant certifications. Since Apple values privacy and innovation, briefly touch on projects where you ensured data integrity or worked with sensitive data.
During the interview, expect technical discussions on distributed systems, data modeling, and pipeline optimization. Be prepared to walk through your experience with Spark, Flink, or similar technologies, and discuss challenges you’ve faced in scaling data systems. Apple values problem-solving, so practice articulating how you’ve debugged data quality issues or improved pipeline reliability. Since the role involves cross-functional collaboration, highlight instances where you worked with ML engineers or product teams. Behavioral questions will likely focus on ambiguity and independence—share examples of how you’ve thrived in fast-paced environments. Finally, research Apple’s commitment to privacy and be ready to discuss how you’ve incorporated similar principles in past projects.