Senior interview questionAsked at Nuro · interview write-up
Design a shared distributed ML training platform with team quotas, coordinated worker placement, versioned data, experiment tracking and checkpoint recovery.
Model answer
Jobs are declarative specs: image, data version, resources. The scheduler gives each team a guaranteed quota and lets idle capacity be borrowed, preemptibly. Gang scheduling places all of a job's workers at once, topology-aware, so no half-started job holds GPUs.
Datasets are immutable, content-hashed snapshots referenced by version. Tracking records config, commit, data version, metrics and artifacts per run.
Jobs write sharded checkpoints asynchronously to object storage, and preemption triggers one; on node failure, the job restarts from the latest. Monitor GPU utilization and queue time per team.
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