Build a mini data loader with sharding for distributed training: split data across workers without overlap.
A build-it-yourself check on distributed input pipelines. What matters is partitioning data across workers with no overlap and no gaps, epoch-consistent shuffling with a shared seed, and handling the uneven-last-batch problem. The code follows.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A build-it-yourself check on distributed input pipelines. What matters is partitioning data across workers with no overlap and no gaps, epoch-consistent shuffling with a shared seed, and handling the uneven-last-batch problem. The code follows.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
