Just finished OpenAI The overall feeling of the interview was: very intense, but not empty. The entire OpenAI interview process is not something that can be covered by just answering questions, but a complete consideration of candidates from theoretical understanding, engineering capabilities to ways of thinking. A brief background: the whole process is online, with a total of 4 rounds. The pace is fast, but each round is quite targeted.
Interview process & practical experience
1. Recruiter Chat
This round is not like a traditional HR interview, but more like a person with a strong technical background confirming what you are interested in and why you do these things.
The most frequently asked questions are:
- What was the last technical deep dive that got you the most energy?
- Where were you stuck at that time and how did you get out?
I don’t care much about how good the results are, but I keep asking you what you think and why you choose the way you do. If you just reproduce it according to the blog/paper, you will basically be questioned.
Reference answer:
I prefer the process of starting from a vague phenomenon and unraveling the problem step by step. Compared with simply working on features, I am more attracted to questions such as "Why does the system behave like this?" The most recent deep dive that I put the most energy into was actually troubleshooting a problem where the performance and stability of a model significantly deviated from expectations after the scale was slightly increased. That time made me realize that if I only stay at the "model level understanding", many problems cannot be explained, and I have to go into the system and implementation details. This also slowly affected my choice of projects later.
2. Coding + ML Debug
This round is the most distinctive round in my opinion.
It’s not LeetCode, nor is it asking you to write Transformer by hand, but it is centered around real ML engineering problems, such as:
- When migrating model weights, the layer does not match. How do you quickly locate it?
- Give you a less than ideal training loop and let you change it to a more memory-efficient version on the spot
The amount of code is not large, but it is a test: have you written real training code, have you stepped on pitfalls, and do you know why you wrote it this way.
Reference answer:
I generally don't change the code as soon as I start, but first confirm whether the exception is a "trend problem" or an "occasional problem." If it is a training-related problem, I will first look at the overall trend of loss and key indicators, and then combine the logs to confirm whether an abnormality has begun to occur at a certain stage; if it is an inference or deployment problem, I will first confirm whether the input distribution and resource usage have changed. At the code level, I will try my best to control variables, such as changing only the batch size, leaving the model structure unchanged, or replacing only one module to observe changes in system behavior.
To me, debugging is more like testing a hypothesis than a quick patch.
3. ML Deep Dive/Research Chat
It basically revolves around the projects on your resume, and drills into the details.
Frequently asked questions include:
- When the loss curve fluctuates abnormally, what is your debugging sequence?
- Why did you choose this loss / optimizer / quantization solution at that time?
- How do you trade off KV cache, quantization, throughput and latency?
Reference answer(Why did you choose this loss / optimizer / quantization solution at that time?):
This choice was made based on two considerations: first, theoretically it could solve the core bottleneck we encountered, and second, the project cost was within controllable range. But after actually running it, the effect was not completely in line with expectations, which is why I later spent time re-analyzing it. My approach at the time was to first confirm whether there was any problem with the assumption itself, and then look at whether the implementation details introduced additional noise. If it is confirmed that the direction is right, but the effect is unstable, I will give priority to finding the reasons from the perspectives of data distribution, optimization process and system constraints, rather than immediately changing the plan.
4. System Design + Collaboration
Prefer system design in real scenarios rather than abstract framework diagrams.
You'll be asked to design a scalable LLM service within constraints, while discussing trade-offs when research goals conflict with engineering costs. In addition to technical capabilities, attention will also be paid to your communication methods and decision-making logic in complex environments.
Reference answer
When I design a system, I usually first clarify the goals and constraints. For example, whether latency, throughput, or cost are more important at the moment, this will directly determine the subsequent choices, rather than drawing the complete architecture at the first step. In terms of implementation, I will try to be as modular as possible, decoupling request scheduling, model execution and caching to ensure that resource utilization can be improved through batch processing and asynchronous scheduling under high concurrency, while using current limiting and priority mechanisms to control tail delays. When researching the conflict between revenue and engineering cost, I will first quantify the trade-off, and then decide whether to conduct scenario-by-scenario or small-scale verification instead of going online in full at once. At the collaboration level, I will clearly explain the decision-making basis and fallback conditions to ensure that the team makes judgments under the same premise.
Some real feelings of preparation
Debug ability is crucial, and interviews focus more on ML + system-level positioning and verification problem abilities, rather than simply writing tricks. Be very familiar with your project and have a clear explanation for every design choice: Why did you do it? What are the alternatives? Will it change if it is redone? It’s okay to make bold assumptions, but the key is to be able to adjust quickly based on feedback. Writing questions is useful, but not enough. The core is to understand the behavior of the model in the real system and be able to debug it. Generally speaking, OpenAI is looking for people who can stably implement cutting-edge research. High intensity, but fun to talk to.
Written at the end: About preparing for this type of high-intensity interview
It is actually very common if you feel unsure when preparing for this type of high-intensity technical interview, especially when you are stuck in deep project digging, system selection, or on-the-spot expression. We also offer positions in major factories Interview assistance , has accompanied hundreds of students to the offer stage in the past and accumulated a lot of real interview feedback. If necessary, please feel free to contact us.
