Recently helped a trainee on the team review his OpenAI VO, the overall experience can be described as "high quality, but also high pressure". The interview lasted about 1 hour and a half and was divided into two modules: System Design + Coding, each with very typical OpenAI style questions, which are described in more detail below.
Background of trainees
This participant's undergraduate degree is in CS at a Midwestern state university, and his graduate degree is in AI at UC Series.
Previous internship experience has focused on backend + ML infra, using Python, Go, C++, and doing simple distributed caching and messaging systems.
Although the algorithm foundation is good, he knows it himself - system design has hardly ever formally answered questions, especially those high concurrency + scaling type scenarios.
His goals were clear when he signed up for our Programhelp:
"I'm not afraid of writing code. What I'm afraid of is the on-site rhythm of being questioned continuously by the interviewer."
On the day of the official interview, we provided real-time support through the voice assist system to help him smoothly stabilize his rhythm.
OpenAI interview process reference
Before sharing specific experiences, let’s first have a general understanding of OpenAI’s interview rhythm:
- Resume screening
After submission, the recruitment team will screen the resume to confirm experience and job compatibility. - Recruiter call
After the resume is approved, there will be a 30-45 minute phone call or video chat to talk about background, motivation and understanding of the position. - Technology assessment
It might be an online programming test or a technical call to test algorithms and basic coding skills, and sometimes there will be preliminary discussions on system design. - System design & in-depth interviews
In the key part, we will give you open architecture questions, allowing you to explain the overall design, component division and trade-offs. - Multiple interview cycles
Usually there are multiple consecutive rounds of interviews, including in-depth discussions on coding, system design, behavior and technology, and testing of technical abilities and communication expression. - Results and Offer
After all links are completed, the team will make a comprehensive evaluation and decide whether to issue an offer.
The entire process can be understood as resume screening → Recruiter call → Technical assessment → System design → Multiple rounds of interviews → Offer, and coding, architecture and communication skills must be taken into consideration.
Round 1: System Design - "Design Slack"
After 2 minutes of opening pleasantries, the interviewer just dropped the question, "Design Slack".
We practiced this question at mock, and the point is to lay out the framework ahead of time without getting bogged down in the details. So he started by summarizing the overall structure:
- Core Components (User, Channel, Message, Notification)
- Data Flow (message → queue → storage → delivery)
- Scaling concern (fan-out, caching, database sharding)
The interviewer listened and immediately cut to the chase, "How would you handle fan-out for large channels?"
He spoke in passing Read-time fan-outWe will also add the "performance trade-off sentences" that we usually train:
"If we choose write-time fan-out, we get better latency for readers but worse scalability for large channels, so I'd rather push fan-out to read time."
At this point, the voice assistants prompted "add a layer of push system", he successfully added notification queue, asynchronous push mechanism.
The interviewer continued to press for database scaling, and he replied with a three-step framework for our training: vertical scale → horizontal shard → async queue.
The interviewer concluded with the comment, "That's a very realistic trade-off analysis."
Summary: The key to success in the first round is pacing. Practicing ahead of time allowed him to talk about the structure in 1 minute, expand on the details in 3 minutes, and get to the trade-off in 5 minutes.
Round 2: Coding - Key-Value Store
The second round is typical OpenAI Engineering Implementation Questions. The title is:
"Design a simple Key-Value Store supporting serialization and deserialization."
He finished first. put,get,delete, logical and clear, writing and speaking at the same time.
The interviewer asked, "How would you persist this data?"
He replied, "I'd serialize the in-memory dictionary into a file using JSON or binary format, then deserialize it back when loading. "
Then follow up to upgrade the difficulty:
"What if each file has a 1KB limit, and you must split the serialized data across multiple files?"
That's when the voice assistant reminds, "Verbalize first, then realize."
He steadied the pace, framing the program as he spoke:
- Chunking data to multiple files (chunking)
- Use a meta index file to record the file number corresponding to the key
- Supports partial load for faster startup
The interviewer nodded in recognition, "Excellent thought process."
Although I didn't have time to write it in its entirety, it was well-structured and clear, and this type of question puts more emphasis on expressing logic.
Interview Pace and Style
The overall experience of OpenAI Electric Surfaces is completely different from the usual big players:
- The questions are open, but the pursuit is very deepEach topic is expanded into engineering-level detail.
- Examining expression goes hand in hand with reflectionYou have to verbalize the code at the same time as you write it.
- time constraintIt's not usually a good idea to give too many hints unless you are actively expressing reasoning.
The feedback from this participant was:
"It was much harder than I thought it would be, but it was also fun. The interviewer was like discussing the design with me, not interrogating. I was able to keep up the pace without panicking."
About Programhelp｜Between you and a big company offer, there is only one reliable assistant missing.
We are a professional team of 7 seniors from Oxford, Princeton, Peking University and other top universities, 3 of whom are now working in Amazon, Google, Ali and other top tier companies. All services - whether OA ghostwriting,VO Assists, interview assistance, the whole Offer package - all done by us personally, never outsourcing. We adhere to the direct connection to the first-line engineers, one-on-one real-time assistance, with real technical strength for you to clear the interview card points.
Whether you are applying for a big factory for the first time, or you want to take a second better Offer, we can provide you with the whole chain of support from OA → Technical Interview → VO → HR Negotiation. 24-48 hours of expedited response, no charge if you don't pass, and then pay the final payment if you get the Offer.
