The OpenAI Forward Deployed Engineer interview loop, round by round
"How do you know it is working?" is the round that decides it.
Three to five weeks, compressed, with a five-hour take-home in the middle. The applied-AI depth is real: evals, RAG internals and production concerns, not familiarity with the API.
Who you meet
- Recruiter
- Engineers
- A solutions or partner lead
- Hiring manager
The rounds
1. Recruiter screen gating
What it grades
- Why Forward Deployed Engineer specifically, not just OpenAI
- Whether you have deployed AI into production and dealt with the mess
Reported as a genuine gate rather than a warm-up. "I have used ChatGPT a lot" is not an answer to the second one.
2. Take-home
What it grades
- Building something real on the OpenAI APIs
- Defending your design choices afterwards
Reported examples: a RAG system, an agent, an evaluation harness.
3. Technical deep dive gating
What it grades
- How you know the system is actually working
- RAG internals: embeddings, chunking, retrieval, reranking
- Fine-tune versus RAG versus prompting, and why
- Guardrails for a production LLM application
- Rate limiting, retries, batching, caching, latency debugging
Evaluation is reported as the deliberate differentiator in this loop. Being vague about how you would measure quality is the common way to fail it.
4. Project deep dive
What it grades
- Architecture of one complex system you built
- Every decision, under fast follow-ups
- How it scales
Interviewers push past the prepared narrative. If the work was not high-scale, have the answer for exactly how it would scale ready rather than deflecting.
5. Hiring manager
What it grades
- Difficult deployments
- Ambiguity
- Explaining technical limits to non-technical stakeholders
Questions reported from this loop
- Why Forward Deployed Engineer specifically, rather than just wanting to work at OpenAI?
- How do you know your AI system is actually working?
- Walk through a RAG architecture in depth: how you pick embeddings, how you chunk, how you retrieve, and whether you rerank.
- When would you fine-tune, when would you use RAG, and when is it just prompt engineering?
- What guardrails would you put around a production LLM application?
- Production concerns end to end: rate limiting, retries, batching, caching, and how you debug latency across the whole stack.
- A take-home of roughly five hours: build something real on the OpenAI APIs. Reported examples are a RAG system, an agent, or an evaluation harness. You then walk through it and defend the design choices.
- Pick one complex system you built, present the architecture, and defend every decision under fast follow-ups. If the work was not high-scale, be ready to say precisely how it would scale.
- Diagnose high latency in an LLM inference pipeline. Walk the whole stack.
What is not on this page: the practice mapped to each round, and the list of what is not worth preparing for this loop. Both are in Rung.
Open this loop in Rung