coreweave·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Apr 2026

Summary

System design round for a Production Engineer role at CoreWeave. Just one question but it had a lot of surface area and I felt like I only scratched half of it before time ran out.

Questions Asked (1)

Design a system that can batch reboot N machines.

System DesignTechnical Trade-offs

Author's notes

Went in thinking this was straightforward and kind of embarrassed myself by jumping to a naive sequential approach first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what does 'reboot' mean (hard/soft), how many machines (N), and what are the constraints (downtime, concurrency, failure handling). Then propose a distributed system with a central orchestrator that batches machines into groups, executes reboots with controlled concurrency, and tracks progress with retries and idempotency. Finally, discuss trade-offs around consistency, availability, and fault tolerance.

Pro tip: Emphasize idempotency and failure recovery: reboots can fail or hang, so design the system to safely retry without causing double reboots or leaving machines in an inconsistent state. Mention using a state machine per machine (e.g., pending, rebooting, success, failed) and a dead-letter queue for manual intervention.

1. Clarify Requirements and Constraints

Ask about N (scale), reboot type (hard/soft), acceptable downtime, concurrency limits, and failure handling expectations. This shapes the design and shows you avoid assumptions.

2. High-Level Architecture

Propose a central orchestrator (e.g., a service) that receives a batch request, divides machines into batches, and dispatches reboot commands to agents on each machine or via out-of-band management (e.g., IPMI). Use a message queue for asynchronous processing.

3. Concurrency and Batching Strategy

Decide on batch size and concurrency level to avoid overwhelming the network or dependencies. Use a rate limiter and consider rolling reboots to maintain availability if machines are part of a cluster.

4. Reliability and Failure Handling

Implement retries with exponential backoff, idempotent reboot operations, and a state machine per machine to track progress. Use a dead-letter queue for persistent failures and provide APIs to query status.

5. Trade-offs and Scalability

Discuss trade-offs: synchronous vs asynchronous, push vs pull, and consistency vs availability. Explain how the design scales with N (e.g., sharding orchestrators, using a distributed queue).

Key Points to Mention

Idempotency: ensure reboot operations can be safely retried without side effects.
Concurrency control: use batching and rate limiting to avoid overwhelming the system.
Failure handling: retries, dead-letter queues, and manual intervention paths.
State tracking: maintain per-machine state (e.g., pending, rebooting, success, failed) for observability.
Scalability: design for large N via sharding, distributed queues, and horizontal scaling.
Trade-offs: discuss consistency vs availability, synchronous vs asynchronous processing, and push vs pull models.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.