Of all the interviews I've had this year, this round withNVIDIAwas the one where I most clearly felt the "technical depth gap.
NVIDIA SDE is not that kind of high-pressure torture, but you can clearly feel it - every question the interviewer asks comes from a pitfall he has actually stepped in, not a memorized question bank.
The whole process takes about 45 minutes, with the first half being BQ with a technical flavor and the second half being live Coding.
Here's what it says in separate segments.
Behavioral side (actually a technical deep dive): every GPU project that has been talked about should be able to stand up to dismantling
The interviewer asked me to pick a GPU / parallel computing related project to talk about. I picked a CUDA kernel optimization that I had done before.
But just as the talk started I realized:
NVIDIA's "Talking Projects" doesn't let you tell a story, it lets you break down the details of the project.
The rhythm of the questions on the other side of the table is probably something like this:
- "What part of the kernel logic are you really responsible for in this project?"
The phrase "generalizing the framework" is blocked. - "What specific metrics do you look at when profiling, SM occupancy, warp divergence, memory throughput?"
If you've seen it, you've seen it. If you haven't seen it, you'll reveal it when you ask. - "How can you tell that the bottleneck is in the memory bandwidth and not the compute unit?"
The question is really looking at whether you know how bandwidth-bound / compute-bound is determined.
The whole process gave me the feeling:
You can do it without making it complicated, but everything you say must be based on real experience.
CUDA Kernel Optimization Ideas: Do you know how to "stack concepts", the interviewer will know!
The second part continues to follow the CUDA optimization question.
These types of questions are asked by many companies, but NVIDIA's perspective is more "engineering". For example:
- When to use shared memory?
- block / grid Why is it cut this way?
- How does warp divergence manifest itself in your project?
- Why is it so optimized? What are the exact gains?
You need to be able to explain "why you do it", not "everyone else does it".
Embedded Performance Bottlenecks: How does trade-off on the Jetson Nano work?
This question I think is a NVIDIA specialty question.
The interviewer asked directly:
"If your model is going to be deployed in a Jetson Nano, how do you balance performance, power consumption, and video memory?"
I mentioned quantization, pruning, kernel fusion, batch size, and so on.
But the interviewer pursued the question in a practical direction:
- How do you determine the current bottleneck?
- How do you know it's memory bound?
- Do you have data? Is there a profiling graph?
I was thinking to myself:
NVIDIA interviewers really do love Nsight graphs ah ......
Coding (shared codepad): the questions aren't hard, but "speaking clearly is more important than writing correctly"
The overall difficulty is not high, but the logic and source of formulas should be explained at each step, especially in the first question.
Question 1: Clock Angle
Give a time like "3:45"Calculate the angle between the hour and minute hands.
The interviewer made a point of emphasizing:
"The formula can't be written directly, to say why it's the formula."
I'll tell it the normal way:
- Minute hand: 6° per minute
- Hour hand: 30° for one hour; 0.5° extra for one minute
- Let's start with two angles relative to 12 o'clock.
- If the difference is >180°, use 360° - diff
After speaking the interviewer nods and throws out follow-up:
"What if it's given to seconds or even milliseconds?"
This is actually a test to see if you can replace the term "discrete angle" with "continuous angle" and explain the angular velocity of each pointer.
Question 2: Multi-threaded Sequential Printing
Three threads A/B/C are required to loop through and print numbers in sequence.
I wrote it in semaphore:
- A Get the license first → Print → Release B
- B Print → Release C
- C Print → Release A
- The counter is protected by a lock to avoid a race condition
Here the interviewer is not concerned with what libraries you use:
Do you really understand how threads "pass execution to the next person".
My biggest takeaway from the NVIDIA interviews during this round
If summarized in one sentence:
NVIDIA wants to know if you've actually written CUDA and profiling, not "know what CUDA is".
Their questions are all about engineering details, not the eight strands.
If you're facing a GPU / Parallel oriented position at NVIDIA in the future, I think the most important thing to prepare for is not LeetCode:
- Real-world experience with kernel optimization
- Understanding of Nsight / nvprof profiling
- How to Determine Performance Bottlenecks
- embedded scenario power/performance trade-off
Coding isn't hard, but clarity in explaining ideas is very important.
The most feared deep digging and chasing, rely on remote assistance to stabilize the
NVIDIA VO's biggest panic is "sudden change of angle + dead-on details" - especially CUDA kernel profiling and embedded scene trade-off, which I had prepared in pieces before! ...
Luckily, I was able to flush it out two days ago. Programhelp 's VO remote assists and the experience goes straight beyond expectations!
The whole real-time voice rhythm, like standing beside a calm to outrageous coach: jam immediately reminded "first speak profiling core indicators" thought deviation on the pull back "to compute-bound vs memory-bound derivation" will also make up "here add a basis, otherwise it will be pursued". "will also make up the knife" here to add a basis, or will be pursued questions".
For those of you who are easily flustered by VO high-density Q&A and don't have a solid GPU/CUDA background, this assist is a real lifesaver, and the clinical performance is directly pulled full circle!
