Forward Deployed Engineer Interview Questions and Answers
Questions from each round of a typical FDE interview loop, with the reasoning a strong answer should show.
mathshian•2 Nov 2026Loop on the next-page cursor or page number until the API returns no more data. Respect rate limits by reading the Retry-After header or backing off exponentially on 429 responses. Mention idempotency, logging each page, and making the function resumable if it fails partway.
Start by profiling the data: count formats, nulls and duplicates. Normalise dates with explicit format parsing, flag rows that fail rather than guessing, and deduplicate on a stable key. Explain how you would confirm assumptions with the customer before dropping data.
Cover ingestion and chunking, an embedding model the customer is allowed to use, a vector store in their environment, retrieval with re-ranking, and an LLM endpoint that meets their data policies. Add access control so users only retrieve documents they can see, plus an evaluation set and logging for quality monitoring.
Clarify which actions are needed and how often. Options include a database integration with strict permissions, RPA on the UI, or a middleware service built with their IT team. Keep a human approval step for writes, log every action, and design a rollback path.
RAG retrieves relevant documents at query time and passes them to the model as context. Use it when knowledge changes often, must be cited, or must respect access controls. Fine-tuning suits changing the model’s style or teaching a narrow task format, not adding fresh facts.
Build a small labelled eval set from real customer examples, define pass criteria per case, and run it on every change. Track accuracy, refusal and hallucination rates, and latency. Review failures by hand and agree on a threshold with the customer before the demo.
Collect failing queries and inspect what was retrieved. Common causes are team-specific vocabulary, documents in unusual formats, or permission filters removing relevant chunks. Fix retrieval first with query rewriting, better chunking or hybrid search, then re-run the eval set.
Acknowledge the request and understand the underlying goal. Explain the impact on timeline and pilot success criteria, then offer options: a later phase, a smaller version, or a trade for something currently in scope. Confirm the decision in writing.
Talk to the users and watch them work. Low adoption usually comes from a workflow mismatch, trust issues with outputs, or friction in access. Fix the biggest blocker quickly, share results with the sponsor, and set a usage goal for the next two weeks.
Use a clear structure: the situation, how you oriented yourself (reading entry points, running tests, tracing a request end to end), what you shipped, and what you would do differently. Interviewers look for a repeatable approach.
Show that you raised it early, came with facts and options, and took ownership. Describe the outcome and how the relationship held up afterwards.