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Design the API for a GPU job scheduler in 45 minutes. What are the resources, states and semantics?

Design job creation, cancellation and listing around retries, durable ownership and an explicit pagination contract.

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Design job creation, cancellation and listing around retries, durable ownership and an explicit pagination contract.

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The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Foundational

📐 AI Systems Design

Control Plane and API Design for GPU PlatformsA control plane records intended work and coordinates its execution. Clear retry, cancellation and listing semantics keep its API useful when requests or workers fail.

Advanced

💻 Coding for Infra🔒 Premium

Asynchronous GPU Buffer OwnershipA host function can return while a GPU still uses its pointers. Tie buffer reuse to device completion, including cancellation, cross-stream consumers and foreign-function boundaries.

Foundational

💻 Coding for Infra

The Practical Coding Screen PlaybookA practical coding interview asks you to build and explain a small system. A clear contract, working core and tests make follow-up changes easier to reason about.

Core

💻 Coding for InfraSign in

Retry, Backoff and IdempotencyRetries can recover transient failures, but they also add load and may repeat side effects. Backoff, retry budgets and idempotency make that behavior explicit.

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UP NEXT ON YOUR JOURNEY

Next in this trackImplement dominant resource fairness for a cluster where jobs need different mixes of GPU, CPU and memory.Next in this trackWrite a tiled matrix multiply and explain the cache effect. Then say honestly what your benchmark actually measured.Next in this trackImplement a log-structured event store for fleet metrics with range queries and compaction.

FEDITOR'S NOTE

Explain concurrent retries and uncertain worker outcomes. Scope the in-memory demonstration separately from durable production guarantees.

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