If you are interested in large models and LLM applications,OpenAI Undoubtedly one of the most desirable companies. However, its technical position interview process is also very "high threshold": not only testing the programming ability, but also emphasizing the in-depth understanding of model principles and engineering implementation. This article is based on real candidate feedback and compiles a complete interview experience to help you save a lot of preparation time.
OpenAI interview process
1. Resume screening: system + manual, core projects should be written in depth and thoroughly, priority will be given to those with AI projects, papers, and implementation experience.
2. Recruiter Call (30 minutes): Evaluate motivation and compatibility. Answers should not be templated and have clear logic.
3. Technical Phone (1-2 rounds, 45-60 minutes each): includes on-site programming, focusing on code quality and thinking process.
4. Deep Dive Interview (60 minutes): Ask about the core project details and understand the project logic and optimization direction.
5. Research/ML understanding: To examine the depth of ML, you need to be able to explain technical principles and real-life scenario choices.
6. Final Round (VO): Half a day to 1 day, multiple rounds of assessments in succession, testing skills, concentration and stability.
Reference Check & Offer: Check past performance and make an offer after passing the review.
Examples of high-frequency real questions
Coding
A candidate encountered this question: To implement an in-memory database that supports basic SQL operations, it is necessary to gradually support SELECT, WHERE, GROUP BY, ORDER BY and JOIN. The question itself is closer to engineering abstraction than pure algorithm, so it very tests the structural design ability.
Candidates who perform better usually first define a unified input format to proactively reduce parsing complexity, then use Map or TreeMap to manage table data, and split filtering, aggregation, and sorting into independent modules. Synchronously building test cases during the implementation process can also reflect mature engineering habits. Interviewers will often continue to observe your design path - whether you have an overall perspective, rather than modifying it as you write.
System design
Another typical problem is: design a multi-tenant CI/CD scheduling system, receive repo ID and commit, parse YAML, and return execution status in real time. When answering this type of question, a sense of structure is often more important than details.
The more common way to expand is to first give the overall architecture, such as accessing the API through the request, entering the execution engine through the message queue, and then writing the state to the storage and pushing it to the front end; then discuss multi-tenant isolation, high availability and load balancing. At the storage layer, you can use Redis or MongoDB to save the state, and use Kafka for system decoupling. As we continue to delve deeper, we will add capabilities such as permission control, information isolation, log query, and failure retry. Excellent answers usually show a "from macro to micro" rhythm of advancement, rather than getting bogged down in technical details from the beginning.
Behavioral interview focus
Many technical candidates will underestimate the behavioral aspect, but in AI companies, this part often carries a lot of weight. The team prefers to find people who can continue to create value in an uncertain environment, not just technical performers.
Topics that interviewers often discuss include: how do you independently supplement knowledge and promote project implementation when resources are limited; when technical differences occur in the team, whether you can use experiments or data to build consensus; and how do you make judgments when technology may have a social impact. There is usually no standard answer here, but true, rational and mature expressions will significantly improve the overall evaluation.
Real case reference
One candidate has a PhD background in CS, and his research focus is on NLP and multi-modality. He has outstanding scientific research capabilities, but relatively insufficient system engineering experience. In early mock interviews, his common problem was not "no", but the difficulty in explaining complex technologies clearly and in a structured way.
In the subsequent preparations, he deliberately strengthened the training of architectural design capabilities, while repeatedly polishing the project expression, upgrading "what was done" to "why it was done and what results it brought." After entering the VO stage, the overall performance became significantly more stable, and I finally got the offer smoothly.
This case actually releases a very important signal: Top AI companies not only value research capabilities, but also pay more and more attention to engineering implementation and technical expression.
Write at the end
OpenAI's interviews are not just more difficult, but the dimensions of the examination are more three-dimensional - focusing not only on technical depth, but also on engineering capabilities, way of thinking, and long-term value matching. Rather than rushing into a temporary interview, it is better to understand the interview structure and question logic as early as possible. This is often more effective than simply answering questions.
We have long compiled real interview experiences and high-frequency question banks from North American AI companies and top technology companies, covering research positions and engineering positions. The repetition rate of many question types in actual interviews is not low. If you establish your cognitive advantage in advance, the preparation process will be more relaxed.
If you want to systematically sort out high-frequency test points, strengthen technical expression, or need Interview help , starting to prepare now will often help more than you think.
