toward the end of NVIDIA The VO round for Software Engineer can be described as "both theoretical and practical". The interviewer did not follow the rules, and the whole process was more like a "logical + engineering" technical discussion.
I'd like to share the complete questions and my summary to help students who are preparing for interviews at NVIDIA or other major hardware-related companies to step on fewer pitfalls.
Behavioral (BQ) - Technical background oriented
Unlike many companies, NVIDIA's behavioral questions don't follow the usual formula, there are no template questions such as "team conflict" or "leadership challenge", but instead are centered directly on technical decisions made in the project. Instead, it centers directly on the technical decisions made in the project.
The first question asked me to describe a project I've done in the GPU or parallel computing direction.
I chose a CUDA kernel optimization project to talk about, and the interviewer's questioning cadence went something like this:
- "What is the module you are responsible for?"
- "What are the main metrics you look at during performance profiling?"
- "How did you determine that the bottleneck was in the memory bandwidth and not the compute unit?"
I could feel that he was not trying to listen to the background of the project, but rather verifying whether you really understand the execution model of GPU. When I mentioned that the optimization method is to reduce global access through shared memory, the interviewer immediately asked, "How do you avoid bank conflict in shared memory?" This kind of question is very detailed, so I suggest that you should be clear about the theoretical basis of each step of optimization when you prepare.
The second question is about ideas for CUDA kernel optimization.
I explained the four dimensions of memory hierarchy, thread-level parallelism, occupancy, and latency hiding, and mentioned using Nsight for analysis. The interviewer nodded his head at this and asked me to add how I verify the effectiveness of the optimization. I mentioned that I would compare it with the quantitative metrics of kernel execution time, memory throughput, and warp efficiency.
The third issue is performance bottlenecks in embedded environments.
The interviewer asked a very realistic question, "How do you find a balance between performance and power consumption when running a model in a resource-constrained environment, such as the Jetson Nano?" I talked about using profiling tools to locate data transfer bottlenecks in CPU/GPU interactions and optimizing the pipeline with mixed precision and asynchronous data streams. the entire behavioral section lasted about 20 minutes and was almost entirely technically oriented questions.
Coding Programming Questions (with Ideas Explained)
After entering the coding section, the interviewer switched to shared codepad and asked me to write and explain the idea on the spot. The topic is not complicated, but it requires to talk about the source of logic and boundary processing.
Question 1: Calculate the angle of the hands of a clock
The question is given a time string such as "3:45" and asks to calculate the minimum angle between the hour and minute hands.
The interviewer made it a point to not write out the formulas directly, but to explain the source of each perspective calculation.
Here's my thought process:
- The minute hand travels 6° per minute (360° / 60).
- The hour hand travels 30° (360° / 12) every hour and a further 0.5° (30° / 60) every minute.
- Calculates the angle of the two hands relative to the 12 o'clock direction based on the hours and minutes entered, and takes the absolute value of the difference between them.
- If greater than 180°, subtract 360° to get the minimum angle.
The interviewer asked after I wrote the code, "How do I adjust the formula if the input contains seconds or milliseconds?" This was a typical follow-up, and I explained that I could convert the time to total seconds, scale the corresponding angle, and then consider the floating point error problem.
He followed up with a very fine point: "How would you correct the results in case of large floating point precision errors?" I mentioned that equivalence could be determined by an error tolerance interval (epsilon), which satisfied him better.
Question 2: Multi-threaded sequential printing
The question asks to design a program that loops through three threads to print numbers in sequence, e.g., A hits 1, B hits 2, C hits 3, A hits 4, and so on.
I was talking about the idea and then writing pseudo-code:
- Use Semaphore to control the order of thread execution.
- Each thread has its own corresponding semaphore, of which only the first thread is licensed at the beginning.
- When the thread finishes printing, release the next thread's semaphore.
- Use locks to ensure atomicity of counter increment operations and avoid race conditions.
The interviewer asked me to continue expanding, "What if it's N threads printing in a loop?" I answered that I could put the semaphore into a loop structure and pass the (thread_id + 1) % N Controls the execution rights of the next thread.
Then he asked, "If one of the threads crashes or gets blocked, does the whole system get stuck? How can I prevent that?" I suggested three ideas:
- Signal timeout release to prevent permanent waiting.
- Introduce a health check thread to periodically check thread status.
- If an abnormal exit is detected, an alternate thread is used to automatically take over the task.
He further asked, "Does your implementation create resource leaks? Does the semaphore have to be destroyed when it exits the thread?" I explained that you can use finally blocks or RAII mechanisms to ensure that the semaphore is released. The whole coding part lasted about 25 minutes, which is a high intensity round.
Follow-up Deep Dive Session
At the end of the two questions, the interviewer spends about 5 more minutes on extended questions.
For example, on the clock question, he asked me to consider the effect of floating-point error accumulation on the results of angle calculations, and whether they could be calculated without using floating-point numbers (e.g., by integrating the processing seconds and then converting them uniformly).
On the multithreading question, he also asked how to ensure the fairness of thread scheduling in a multicore environment, and mentioned some system-level issues related to lock starvation and spinlock. The overall feeling is that he is not only testing the algorithms, but also the stability and robustness of the engineering implementation.
Interview summary
NVIDIA has a very distinctive interview style with two core characteristics.
The first is to emphasize the process of deriving algorithms.
In the case of the clock angle question, for example, even if the question itself is not difficult, the interviewer wants you to be able to mathematically explain the basis for each step of the calculation and be able to flexibly adjust the formula according to changes in demand. In other words, they don't want to "memorize" the question, but to "derive the logic".
The second is to emphasize the robustness of the system design.
The multithreading questions test not only how concurrency control is written, but also focus on whether you have considered details such as thread exceptions, deadlock prevention, and resource release. This part is where many candidates tend to lose marks.
In terms of the overall pace, this round of VO has very high requirements for oral expression, you must write and speak clearly while thinking, otherwise the interviewer will interrupt to ask follow-up questions. The whole interview experience is hardcore, but very logical, the interviewer's attitude is professional, the pace is tight, and there is no small talk.
Advice for preparing for an NVIDIA interview
First, prepare ahead of time for a quantitative project experience, especially related to GPU optimization, parallel computing, and system performance tuning.
Second, algorithm questions should not be memorized formulas, but should be able to be derived and extended.
Third, threaded questions should consider exception and recovery mechanisms, especially system resource management details.
Fourth, oral explanation ability is very important, NVIDIA engineers attach great importance to the clarity of your logical expression.
If you're also preparing for a position at NVIDIA, AMD, Qualcomm, Tesla Autopilot, or Apple GPUs, this type of interview is basically the same. It's not about obscure algorithms, it's about being able to translate a "problem into a verifiable engineering solution".
When well prepared, these types of interviews are completely solid.
How to Efficiently Prepare for Hardcore Technical Interviews Like NVIDIA's
These interviews have high requirements for algorithms, system design and presentation skills, and many students get stuck on "logic" or "incomplete implementation". Especially NVIDIA, AMD, Tesla such engineering-oriented companies, the interviewer will often go deep into the underlying principles of implementation, once the derivation or details do not understand, the rating will drop directly.
We are in Programhelp have accompanied hundreds of international students to go through similar VO practice in big factories, including NVIDIA, Apple, Meta, Tesla, Google and other positions, and accumulated a large number of first-line questions and interviewer's question logic. If you are also preparing for big factory interviews, but you always feel that you are easy to get stuck, your speech is messy, and your logic is not smooth, then our Interview Assist/VO Assist/Interview Helper service can surely help you.
The Programhelp team consists of a number of former Amazon, Meta, and Stripe engineers who specialize in providing one-on-one remote voice assistance and real-time interview coaching. Whether it's an Online Assessment (OA) or a Virtual Interview (VO), we can help you keep your thoughts clear and your pace steady while answering questions by means of seamless on-line voice reminders, so that you can solve the stuck points without revealing them.
