Mistral Forward Deployed Engineer interview questions

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.

Why a raw base model answers your question with three more questions, what RLHF actually buys, and the niche cases where customers should pick base. A tidy answer most candidates fumble.

The data-volume thresholds, the break-even math, and the regression-prevention checklist that turn 'just fine-tune it' into a defensible engineering decision. Includes the crossover rule interviewers listen for.

Zero retention, private endpoints, redaction pipelines, or open weights in their VPC, the four-rung ladder for PII-sensitive deployments, and the discovery questions that pick the right rung.

10x doesn't come from one trick. It's multiplicative: routing x caching x prompt diet x batching x distillation. The stacked-levers playbook with rough multipliers, and the eval gate that keeps quality honest.

Adapter math, the VRAM arithmetic that makes 4-bit a 4x unlock, and the honest decision rule for on-prem deployments. The deep-internals question Mistral and Cohere loops use to find real practitioners.

The GPU-utilization math that decides this argument, the hidden line items (ops headcount, quality gap, model churn), and the hybrid recommendation that wins both the analysis and the customer.

Two compression levers with very different bills. The arithmetic that decides between them, when to stack both, and the eval discipline that keeps a 'cheaper' model from quietly tanking your hardest 10% of queries.

Rotary embeddings encode position as a rotation of the query and key vectors, so attention scores depend only on the gap between two tokens. That relative property is why RoPE generalizes past its training length where learned absolute tables fall off a cliff.

64 H100s for 30 days is a real budget with a real token count. The answer sizes the run from Chinchilla, picks parallelism for this exact cluster, and has a concrete plan for loss spikes and silent data corruption.

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.

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

'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.

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.

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.

A reported FDE design question that punishes one-size-fits-all answers. Each corpus has a different 'semantic unit': get the three designs, the parsing tools to name, and the eval that proves the split was worth it.

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 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.

Pure vector search fails on the exact queries enterprise users ask most: IDs, error codes, product names. Here's the failure-mode framing that turns a definition question into a design answer.

Anyone can list mitigations; the question is really about costs: latency, dollars, and lost recall. The three-layer answer with honest price tags that AI-lab interviewers score highest.