如果你是大模型算法的面试官,你会问哪些问题? (English)

Generated: 2026-06-20 11:20:53

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Okay, here is the translation into English, preserving the storytelling style and the conversational, direct tone of the original.

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You know, I've interviewed over two hundred candidates, from Google big shots to fresh-faced new grads. Every time I sit down for an interview, what scares me most isn't that they can't answer a question—it's that they answer it too perfectly. You can tell it's just memorized boilerplate, with a kind of AI-generated drift to it. The other day, a candidate flawlessly recited the definitions of data parallelism and model parallelism. So I asked him, "So what batch size did you actually set? Where was the communication bottleneck between your cards?" He froze completely. You see, interviews are not something you can just cram for.

Today, I'm not going to talk abstract theory. I'm going to break down, one by one, the questions I actually ask. Ready? Don't be surprised if you start questioning your own knowledge, but I guarantee you'll want to try this stuff out yourself by the time you finish reading!

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1. Let's start with "Distributed Systems", right where it hurts.

I always ask this one: "What's the difference between Data Parallelism and Model Parallelism?"

Don't laugh! The number of people who mess this up is enough to make me question reality. Someone can recite the definition—"Data parallelism loads the same model on multiple cards, each card processes a different batch of data, then synchronizes gradients via AllReduce after computing." Sounds right, doesn't it? But think about it—if the interviewer actually buys that canned answer, then what's the difference between this person and someone who just read a blog post?

I always dig deeper: "What kind of landmines have you actually stepped on?"

I've stepped on a huge one myself. When I first got into distributed training, I thought training on 64 cards just meant running the same single-card process 64 times. Then I used DeepSpeed's ZeRO-3 for the first time and completely messed up model parallelism. The model parameters were split across the different cards, and the communication overhead during the forward pass was through the roof. The training ran for three days. The loss exploded. I stared at those loss curves for three days, and I wanted to just slap myself silly.

So when we talk about distributed systems, I need to know: Are you actually doing this for real, or did you just read a blog post and now you're trying to BS me?

That's why every time the topic comes up, I have to ask about the specifics:

• Were you using DP or DDP? The difference is huge. DDP maintains its own optimizer on each card. DP has to collect gradients in a single process. The efficiency gap is an order of magnitude.
• What problems do the three stages of ZeRO actually solve? Don't just tell me Stage 1 only optimizes the optimizer state. I want to know: "When you actually used ZeRO-3, and you sharded the model parameters across the cards, how did you optimize the communication overhead?"
• If you only have two machines with two cards each, would you use Tensor Parallelism or Pipeline Parallelism? Don't just tell me either is theoretically possible. In practice, with two cards, using Tensor Parallelism means you have to do an AllGather for every single layer. The communication overhead will drive you insane. I tried it. The training was three times slower.

Here's the thing. You might think this is a technical selection issue, but at its core, it's an engineering trade-off. In large model training, there is no "best" solution, only the least terrible compromise.

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2. Transformer Architecture: The more basic it seems, the easier it is to screw up.

This is my go-to for filtering out people who just memorize answers: "Explain why Transformer needs Layer Norm instead of Batch Norm."

90% of people give the textbook answer: "NLP sequences have variable lengths, Batch Norm is sensitive to batch size." Is that answer correct? Yes. But think about it—if someone trained a CV model with Batch Norm and it completely blew up, and they came to you to complain, would your only response be, "Well, you can't use it for NLP"?

The real key is this: The deep structure and residual connections of the Transformer make Layer Norm more stable.

Specifically, Batch Norm normalizes across the entire batch. But the output distribution of each layer in a Transformer changes dramatically—think about it, after adding the residual connection, the outputs from layer to layer are like a rollercoaster. The statistical estimates Batch Norm relies on simply can't keep up. Layer Norm normalizes each sample independently. It's not affected by other samples in the batch, so it's much more stable.

Some people can even write out the formula. But then I ask, "Why does LLaMA use RMS Norm instead of standard Layer Norm?" and they get stuck.

Interestingly, LLaMA uses RMS Norm because it drops the mean operation and only keeps the variance. Why can they do that? Because the residual connections in the Transformer inherently provide a bias offset, making the mean normalization redundant. By cutting this step, the forward pass gets a tiny bit faster. For a model with hundreds of billions of parameters, that's not a small saving. Think about it—hundreds of billions of parameters, saving one mean calculation per layer, the whole forward pass speeds up by a few percent. Is this a math problem? No. It's the art of engineering efficiency!

So, you might think Layer Norm is the standard for NLP, but then LLaMA comes along, drops even the mean, and trains perfectly fine. The more fundamental something seems, the more you have to ask: "Why must it be this way? Can we do without it?"

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3. RLHF and Evaluation: The part I care about most.

Honestly, in interviews, I love asking about RLHF. It's the best way to see if someone truly understands something or if they're just parroting it.

I once asked: "Why does Chain-of-Thought (CoT) work? What are its side effects?"

The standard answer is quick to come: CoT makes the model "think step by step," using more tokens to buy computational depth. Yes, that's the gist. But have you considered this: If a model performs great with CoT on the test set, but its response time in production is three times slower, how are the users going to react?

Believe me, I've been cursed out over this! When I actually deployed it, I found that CoT is not only expensive, it can also leak your system prompt. One time, I asked a model to solve a math problem. It started its reasoning by directly outputting, "You are the assistant developed by OpenAI..." I was stunned on the spot. Problems like this never get mentioned in papers, but in production, they are fatal.

So I follow up with: "How do you control the side effects of CoT?"

My personal experience: In the prompt, I add boundary constraints for CoT. For example, "Think step by step, but do not output the reasoning process" or "Put any chain-of-thought reasoning inside tags." Using this method, I compressed the token length down to 60% of the original, and the system prompt never leaked again.

You see, you might think CoT is a great tool for boosting reasoning, but without controlling its boundaries, it's a ticking time bomb in production. Everything that looks great in a paper turns into a minefield once it goes live.

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4. Evaluating Fine-tuned Models: This is where the real skill is.

I use this question to test practical experience: "After you finish optimizing your model, how do you evaluate it?"

If the answer is just, "Look at BLEU or ROUGE," you're pretty much out. Why? Because there is a vast chasm between offline metrics and the online user experience.

For applied roles, the real assessment is Case Analysis:

Pull a set of samples that showed significant improvement. Explain clearly why they improved. Then pull another set that didn't improve. Explain why they didn't improve, and what your next steps for improvement are. If that type of sample doesn't improve but also doesn't affect the business value, you don't even need to mention it.

Last month, I was optimizing