What to Expect in the CoreWeave System Design Interview

Expect a design round inside the final loop, based on CoreWeave's own product. CoreWeave runs a cloud built for training and serving AI models on GPUs. Candidates report role-specific questions, not standard social network questions.

Published interview guides list four question types. They are a multi-tenant GPU cluster scheduler, a Kubernetes control plane for GPU nodes, and high-throughput storage for training data. The fourth is a service that keeps working during a regional outage.

Multi-tenant means many customers share one system safely. Reliability during failures is graded as much as the architecture. Some hardware and firmware candidates report no design round at all, so confirm with your recruiter.

The Question Types

GPU cluster scheduling. Design a system that places training jobs on GPUs across many machines. The hard part is topology. Topology is the layout of which GPUs are connected by fast links.

A job must be placed on GPUs that can exchange data quickly. Jobs also need gang scheduling: all of a job's GPUs start together, or none do.

A Kubernetes control plane for GPU nodes. Kubernetes is the open source system that schedules containers on many machines. CoreWeave runs it directly on bare metal.

Questions here cover device plugins, which tell Kubernetes about GPUs, and custom resources, which extend Kubernetes for new kinds of hardware. Expect questions about health checks and replacing failed nodes.

High-throughput storage for training data. Thousands of GPUs read the same dataset at the same time. Design storage that serves that load without slowing the job.

Expect to discuss caching, parallel reads, and checkpoint writes. A checkpoint is a saved copy of a model partway through training.

Availability during a regional outage. Design a service that keeps working when one whole data center region fails. This tests replication, failover, and how you avoid losing customer jobs.

What the Interviewer Grades

Practical judgment matters more than exotic parts. State requirements first, including scale and failure cases. Name your trade-offs out loud.

Connect choices to the customer: a stopped training job wastes thousands of expensive GPU hours. Candidates report interviewers who know the domain well, so defend each choice with a reason.

Concrete Kubernetes knowledge earns credit. Vague references to "the orchestrator" do not.

A Walkthrough: Design a GPU Job Scheduler

Here is a high level plan for the signature question.

1. Requirements (5 minutes). Many customers, each with private jobs. Jobs need 8 to several thousand GPUs. Targets: high GPU utilization, fair sharing between customers, and fast recovery when hardware fails.

2. The topology model. Store the cluster as a tree. GPUs inside one server share the fastest links.

Servers in one rack share a fast network switch. Racks connect through slower links. The scheduler prefers placements low in the tree.

3. The queue and placement. Each customer has a queue with a quota. The scheduler picks the next job by priority and fairness.

It then searches for a set of free GPUs that fits the job inside one topology domain. If none fits, the job waits rather than running slowly across bad links.

4. Gang scheduling. Reserve all GPUs for a job before starting any of them. Release the reservation if a part fails to start. This avoids half-started jobs holding GPUs while waiting for the rest.

5. Failure handling. Health checks run on every GPU and network link. When one fails, mark it unhealthy, notify the job, and let it restart from its last checkpoint on a replacement. Track failures per machine so bad hardware is removed from the pool.

6. Scale and observability. Keep the placement decision fast with cached free-GPU maps per domain. Record every placement decision so a customer can ask why a job waited.

Report utilization and goodput per customer. Goodput is the share of time spent on useful training work.

Common Mistakes in This Round

• Ignoring topology. A scheduler that treats all GPUs as equal fails the question. Placement quality is what the question tests.
• No failure story. Hardware fails constantly at this scale. If your design has no plan for a dead GPU, it is unfinished.
• Skipping multi-tenancy. Customers must not see or slow each other. Mention quotas and isolation before the interviewer asks.
• Starting with the model. The interview is about the system around the model: scheduling, networking, storage, and recovery.

How to Prepare

• Learn the building blocks. Grokking the System Design Interview covers queues, caches, and replication, which every scheduler design uses.
• Go deeper on failure cases. Grokking the Advanced System Design Interview helps with replication, consensus, and recovery.
• Rehearse the scheduler walkthrough. Practice the six steps above out loud in under 40 minutes.
• Read CoreWeave's own engineering posts. Its posts on running Slurm on Kubernetes and on fast model loading show which problems the team cares about.
• See the full loop. The design round is one part of the CoreWeave interview process, next to the motivation question. The waits between rounds are in How long it takes to hear back after a CoreWeave interview.