Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

OpenAI SWE interview with a distributed systems problem that had almost no constraints given upfront. You had to implement a message handler and basically define the problem yourself as you went.

Questions Asked (1)

You're given a distributed system with a known machine count and a network topology. Implement a receiveMessage function that updates local state when a machine receives a message and delivers it according to appropriate semantics. No constraints or examples are provided.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The part that tripped me up was there were no delivery semantics specified, no examples, nothing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the missing constraints—message delivery semantics (at-most-once, at-least-once, exactly-once), ordering guarantees, and failure model—since they fundamentally shape the implementation. Then design a receiveMessage function that updates local state idempotently and delivers messages according to the chosen semantics, using sequence numbers, deduplication, and acknowledgments as needed. Finally, discuss trade-offs between consistency, latency, and complexity, and how the topology (e.g., star, mesh) influences routing and failure handling.

Pro tip: Demonstrate maturity by explicitly stating your assumptions and asking clarifying questions before diving into code; interviewers value candidates who identify ambiguity and reason about trade-offs rather than jumping to a solution.

1. Clarify requirements and constraints

Ask about delivery semantics (at-most-once, at-least-once, exactly-once), ordering guarantees, failure model (crash-stop, Byzantine), and network reliability. Confirm the topology and machine count to understand routing and potential bottlenecks.

2. Define local state and message handling

Specify what local state is needed (e.g., last processed sequence number, deduplication cache, pending acknowledgments) and how receiveMessage updates it atomically. Ensure thread-safety if concurrent messages can arrive.

3. Implement delivery semantics

Based on chosen semantics, implement logic: for at-least-once, use acknowledgments and retries; for exactly-once, combine deduplication with idempotent processing. Handle out-of-order messages with buffering or sequence numbers.

4. Integrate with topology and failure handling

Consider how messages are routed (e.g., gossip, direct), and how failures (node crash, network partition) affect delivery. Implement retries, timeouts, or fallback mechanisms as appropriate.

5. Discuss trade-offs and optimizations

Analyze trade-offs: exactly-once adds overhead but simplifies application logic; at-least-once is simpler but requires idempotency. Mention potential optimizations like batching, piggybacking acks, or using vector clocks for causality.

Key Points to Mention

Delivery semantics: at-most-once, at-least-once, exactly-once and their implications
Idempotency and deduplication techniques (e.g., sequence numbers, message IDs)
Ordering guarantees: FIFO, causal, total order and how to achieve them
Failure handling: retries, acknowledgments, timeouts, and idempotent operations
Topology-aware routing: direct vs. gossip, and impact on latency and reliability
Trade-offs: consistency vs. availability, latency vs. durability, complexity vs. correctness

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