Hippocratic AI Forward Deployed Engineer interview questions

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.

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.

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.

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.

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.

Every deployment has a skeptic, and 'I showed them data and they came around' is the answer everyone gives. The scored version starts with why they were right to be skeptical.

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

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.

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.

JSON mode, strict schemas, constrained decoding, validate-and-retry: the escalation ladder for structured output, plus the catch nobody mentions: schema-valid can still be factually wrong.

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.

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.

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 same operation scores attention and ranks your RAG results. Knowing exactly what the dot product measures, and when its magnitude term quietly breaks your retrieval, is what separates a working pipeline from a mysteriously bad one.

TTFT vs tokens-per-second, the output-length lever everyone forgets, and why streaming is the highest-ROI fix that changes no latency at all. The five-lever answer with real numbers.