OpenAI Forward Deployed Engineer Interview Experience
OpenAI
The weirdest part was that the take home was basically the job. I had to build a semantic search setup over Amazon products for ChatGPT, then walk that same solution back to the team because that is basically what they work on.
Interview process
An OpenAI recruiter reached out to me directly, and the process was pretty focused compared to a normal SWE loop. The main pieces were a one-week take home, a live team discussion of that case study, and a separate AI-enabled coding screen that was basically an easy LeetCode problem. My take home was to build a semantic search setup over Amazon products for ChatGPT, and a lot of the evaluation seemed to come down to whether I could explain my decisions clearly and tie them back to customer needs. The coding round itself was not bad at all, but they still wanted me to speak to time complexity and memory. I made it to the onsite stage.
Interview rounds · 4
- 1
Recruiter screen
BehavioralAn OpenAI recruiter reached out to me directly, gave me the rough process, and the big thing was that the loop centered on a take home plus a live coding screen rather than a bunch of generic rounds.
- 2
Take-home assignment
Case StudySystem DesignArtificial IntelligenceCustomer InteractionThe take home was the core of the process for me. I had a week to build something real, submit the code plus a working app, and then walk my choices through a team member.
Q1. Build a semantic search system over Amazon products so ChatGPT could access it.
How they answeredI built the case study around semantic search for Amazon products and submitted both the code and the app. In the walkthrough, I focused on why I made certain design choices, how specific I wanted retrieval to be for different search intents, and how I would tweak the system based on what the customer actually needed. The discussion felt less like trivia and more like them checking whether I could explain the solution cleanly, understand the use case, and show that I was not just shipping slop.
Follow-up questions- How are you thinking about the use case for this?
- Where would you make decisions to take a certain path or not?
- How specific do you want to be on certain searches?
- 3
Technical round
CodingData Structures & AlgorithmsTechnicalThe live technical screen was pretty chill. It was an AI-enabled coding round and felt like an easy, multi-step LeetCode problem with a little pressure on efficiency.
Q1. Solve this delivery-rate style coding problem in the live coding screen.
How they answeredI treated it like a straightforward easy LeetCode question and worked through the multi-step logic live. Since the environment was AI enabled, the real signal felt less about memorizing tricks and more about whether I could still reason through the solution. They specifically asked me to talk through time complexity and memory efficiency, so I made sure to explain the runtime and space tradeoffs instead of just getting to working code.
Follow-up questions- What is the time complexity?
- How efficient is your solution on memory?
- 4
Final / onsite round
Project DiscussionCross-FunctionalBehavioralCustomer InteractionI did not end up doing the onsite because I took another role, but I was told it would be a panel with about four interviewers plus a hiring manager, mostly centered on walking through the same solution again from different angles.
Tips from the candidate
If I were prepping for this, I would not over-index on hard LeetCode. I would still do coding prep, but I would spend way more time practicing how to explain technical choices in plain English, especially in a customer-facing context. For this kind of FDE loop, you need to be able to show both technical grasp and customer empathy at the same time. I would also practice case-study style system thinking, because they really care whether you understand the use case and can adapt the solution when the customer's needs change.
Company culture
This process felt very tied to the actual job instead of being abstract interview theater. They seemed to care a lot about whether I could build something clean, deploy it properly, explain it well, and adjust it around customer needs. Even the coding screen being AI enabled told me they are less interested in pretending tools do not exist and more interested in whether you can still reason about efficiency and write solid code. For FDE there, it felt like they want somebody they can trust in front of a customer and trust to go build the thing without a ton of hand-holding.