Sardine Forward Deployed Engineer interview questions

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.

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.

Every FDE loop opens with this, but the definitions are table stakes. What gets scored is whether you can map each metric to a dollar cost the customer recognizes, here's the framing that does it.

OWASP's LLM01 is the opening question of nearly every AI security screen. Most candidates can define direct injection; the ones who get hired explain why indirect injection is the one that ruins enterprise deployments.

This is a discovery question wearing an ML costume. The strongest candidates run a four-step translation from business pain to loss function, and put a number on each error before choosing anything.

A 0.95 AUC model can still flood a review queue with garbage. Knowing when ROC AUC flatters a model, and what to report instead, separates FDEs who've shipped from those who've only run notebooks.

The textbook answer gets a polite nod. The version that names which side of the tradeoff a 6-week customer pilot usually dies on gets the hire signal, here's that version.

The standard AWS ML Engineer opener. Most candidates recite 'data and model versioning' and stall, the answer interviewers score highest explains why ML tests can't be pass/fail and what continuous training adds as a third loop.

Candidates use these terms interchangeably and security interviewers notice immediately. The distinction, whose policy is being violated, drives completely different defenses, and that's the actual question.

This exact gap kills more customer pilots than any modeling choice. There's a ranked list of culprits, and one of them hides inside innocent-looking feature pipelines at almost every enterprise.

Every RAG deployment runs on this math, and most candidates can't say why cosine beats Euclidean, or when the two are secretly identical. The 90-second version with a worked example.

Time-series is where standard ML habits, random splits, fancy models first, single-number forecasts, fail loudest in front of customers. The baseline discipline and backtesting setup that keep you credible.

There's no trick here, except that interviewers are listening for one specific word: a plan. The candidates who fail give enthusiasm; the ones who pass give logistics.

The 'thanked you later' clause is the trap, it rules out both the pushover story and the stubborn-engineer story. What's left is a specific skill, and interviewers know exactly what it sounds like.

The most common objection in enterprise AI, and arguing back is the fastest way to fail it. The winning sequence is validate, diagnose, reframe, de-risk. Here's the script.