Perplexity MLE Interview Questions (2026)

Questions reported from Perplexity machine learning engineer interviews. This bank tracks 13 questions asked at Perplexity (7 with free model answers). Practice them live with DeepOffer's AI voice interviewer.

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#53 Compare greedy, beam, top-k, top-p, and temperature decoding, and identify when each one fails. ★
#173 Define TTFT, TPOT, ITL, and throughput, and explain how they trade against each other. ★
#177 Your p99 latency doubled after a deploy with no model change — walk through the diagnosis. ★
#216 Decide between a sparse BM25 retriever and a dense retriever, and explain when you need both. ★
#217 Explain what a reranker is, when to use one, and what a cross-encoder costs you. ★
#223 Design the ranking system combining BM25, dense retrieval, and LLM reranking across multiple indexes.
#224 With 50 retrieved passages but a useful context budget of ~10, explain how you choose and how you know the choices are good.
#225 Design an answer engine that returns a cited, streamed answer within a 3-second end-to-end budget.
#236 Explain how an answer engine handles breaking news — a query about something that happened 20 minutes ago.
#244 Design an LLM-as-judge evaluation, name its known biases, and explain how to correct them.
#288 Design an LLM gateway with routing across providers, failover, caching, budgets, and rate limits. ★
#322 Write an async batch processor over an API with concurrency limits, retries with jitter, and error isolation. ★
#396 Explain what makes a Perplexity answer great versus mediocre, where it loses to traditional search, and what you would ship to fix it.