Baseten 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.

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 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.

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

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.

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.

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.

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.

A classic metrics question with a domain twist. The answer isn't 'recall'; it's a cost-of-errors argument plus a threshold strategy, and interviewers grade the reasoning, not the metric you pick.

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.

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.

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.

A Fermi estimate with a paycheck attached. The interviewers' favorite filter: candidates who jump to a dollar figure miss the two numbers that actually break deployments, peak QPS and tokens-per-minute limits.