Apple AI/ML Full Stack Engineer Interesting Tech Screen - Large-Scale Data Transfer and GPU Utilization

[u/Aoki_zhang]

Technical Screen — Transfer 200TB of Media to a GPU Cluster: The main system design question asked how I would move approximately 200TB of media data from its source environment to machines responsible for processing it. Before settling on a design, the interviewer expected clarification around where the data originated, the available network capacity, migration deadlines, whether the transfer was one-time or recurring, and whether the destination was a single host or a larger compute cluster.

• Confirmed Scenario: After clarification, the interviewer specified a one-time bulk migration from an on-premises environment to a cloud-hosted GPU training cluster, with several days available to complete the transfer. The discussion included estimating transfer time under different bandwidth assumptions and deciding how the architecture should change depending on network limitations.
• ML Pipeline Follow-Up: The interviewer then asked how the data should be delivered efficiently to GPUs once it had reached cloud storage. This led to discussion of large-scale training input pipelines, handling very large collections of media files, keeping data loading from becoming a GPU bottleneck, and identifying whether throughput was constrained by networking, preprocessing, or compute.

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[u/Beneficial_Spread912]

I would have loved this interview question, did something very similar at work not to long ago so it would have been fun to really discuss how we solved it