Decagon·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026Remote

Summary

Had a system design-flavored behavioral round at Decagon for a software engineer role. The whole thing was basically one big open-ended question about voice AI architecture, which sounds cool until you're actually in it trying to remember what you know about ASR pipelines on the spot.

Questions Asked (1)

If you were designing a real-time AI voice system from scratch, what engineering decisions would you make across the full stack? Think streaming speech-to-text, text-to-speech, turn-taking, latency budgets, pipeline architecture, and on-device versus cloud tradeoffs, and how you'd prioritize given real constraints.

System DesignTechnical Trade-offsRoadmap Prioritization

Author's notes

This is a massive question and I kind of froze for a second figuring out where to even start.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., latency target, deployment environment, scale) to frame your design. Then walk through the full pipeline—streaming STT, turn-taking, TTS—highlighting key engineering decisions and trade-offs at each stage. Finally, discuss prioritization and how you'd iterate given real-world constraints.

Pro tip: Anchor your answer around a concrete latency budget (e.g., sub-300ms end-to-end) and explain how each component fits within it—this shows you think in terms of user experience and system constraints, not just isolated technologies.

1. Clarify Requirements and Constraints

Ask about target latency, deployment environment (on-device vs. cloud), scale, and use case (e.g., voice assistant, customer support). This ensures your design is grounded in real needs.

2. Design the Streaming Pipeline

Outline the end-to-end flow: audio capture → streaming STT → NLU/dialog management → TTS → audio playback. Emphasize streaming at every stage to minimize latency.

3. Address Turn-Taking and Interruption Handling

Explain how you'd detect end-of-turn (e.g., VAD, prosody, silence thresholds) and handle barge-in, ensuring natural conversation flow.

4. Make On-Device vs. Cloud Trade-offs

Compare latency, privacy, cost, and accuracy for each component. Propose a hybrid approach where appropriate (e.g., on-device VAD, cloud STT/TTS).

5. Prioritize and Iterate

Given constraints, prioritize the most impactful components (e.g., STT latency, TTS naturalness) and outline a phased roadmap with metrics for success.

Key Points to Mention

Streaming STT and TTS to achieve low latency (e.g., chunked audio, incremental transcription).
Turn-taking detection using VAD, silence thresholds, and prosodic cues; handling interruptions.
Latency budget breakdown: allocate milliseconds to each stage (e.g., 100ms STT, 50ms NLU, 100ms TTS).
On-device vs. cloud trade-offs: privacy, cost, latency, model size, and accuracy.
Pipeline architecture: microservices, message queues, and backpressure handling for scalability.
Prioritization framework: impact vs. effort, user experience metrics (e.g., response time, naturalness).

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