Retell AI Forward Deployed Engineer interview questions

Both payloads parse to the same object. Only one authenticates. Trace the bytes, endpoint secret and timestamp before replaying the customer's missed events.

Deduplicating by call ID drops the analysis event. Replacing the whole record can erase it. Design the receiver around separate facts and explicit business effects.

The warm-up that filters out tutorial-level candidates. Interviewers aren't grading the diagram, they're grading whether you know the three places real pipelines fail and how you'd see it happening.

The warm-up that quietly filters out candidates who only know LLMs as a magic API. Nail the two-phase inference picture and you set up every later answer about latency, cost, and hallucinations.

Everyone can define it; few candidates know why 'advertised context' and 'usable context' aren't the same thing. The cost, latency, and attention caveats that make this answer stand out.

Modern LLMs share one architectural skeleton. Learn the five components and you can read almost any model card cold, plus the single distinction that proves to an interviewer you actually understand it rather than the buzzwords.

Everyone says '500 tokens with overlap.' The candidates who get hired explain why chunk boundaries are a recall problem, name three strategies, and describe the experiment that picks between them.

Every FDE will face an angry customer asking this. The mechanistic explanation interviewers want (why hallucination is the default behavior, not a bug) plus the framing that defuses customer escalations.

Every queue, webhook, and retry loop you'll ever deploy at a customer hides this distinction. Interviewers use it to separate people who've shipped from people who've read about shipping.

Tokens drive your customer's bill, their latency, and the weird failures like botched arithmetic. The rough numbers every FDE should rattle off, plus the trap answers interviewers listen for.

A deceptively simple sampling question with one trap (does temperature 0 fix hallucinations?) and one senior move interviewers reward. Know which workflows want zero.

The single most common FDE behavioral question, and the 'week one' follow-up is where most candidates collapse. Here's the structure that signals you can be dropped into chaos and produce order.

This is a flight-risk filter, not small talk, FDE teams lose hires back to pure SWE roles constantly. The answer that passes is built on evidence, not enthusiasm. Here's what it sounds like.

'Evals first' is the #1 hiring signal in FDE design rounds. This is the foundational version: what a 50-case golden set looks like, which metrics split retrieval from generation, and the launch gate that convinces interviewers.

Sierra's signature design exercise. The grading hinges on three things most candidates underweight: tool API shape, the read/write trust boundary, and eval cases written before the agent. Walkthrough inside.

The concept underneath every RAG system you'll ever deploy. How to explain vectors, cosine similarity, and the one limitation of embeddings that costs real deployments accuracy.

The retrieval design question with a precise rubric: candidates who can say what each stage rescues, and what it costs in latency, clear it. Includes the RRF detail and the latency budget interviewers ask for.

A staple at OpenAI and Anthropic in both coding and design rounds. Most candidates can name the algorithms; few can say which one lets 2x your limit through, and that's the part that gets scored.