I successfully received an offer from Nvidia (US), and I’d like to share my complete interview experience here. Hopefully, this can serve as a reference for those who are preparing for interviews at Nvidia or similar companies.
The first round was a phone interview with the hiring manager. This round mainly focused on my background and fundamental skills. At the beginning, I was asked to give a relatively complete self-introduction, including my academic background, core courses I had taken, and several projects I had worked on. After that, the interview moved on to coding-related topics, which included both conceptual questions about programming and concrete coding problems. Toward the end, the interviewer introduced the team’s work and asked several behavioral questions (BQ). The overall pace was steady, but the amount of information covered was quite substantial.
The second stage consisted of technical interviews with engineers from the team. There were three rounds in total, conducted in an on-site–style phone interview format, and the difficulty level increased noticeably. These interviews were highly intensive, with frequent back-and-forth questions covering a wide range of topics. The discussions involved containerization and DevOps-related knowledge, as well as tools used at different stages of the software development lifecycle. Interviewers also paid close attention to candidates’understanding of design concepts, such as code design principles and naming conventions, which reflect real-world engineering practices.
From a problem-solving perspective, the interviews mainly revolved around coding ability, including C/C++ fundamentals, data structures and algorithms, and a certain amount of system design questions. During my preparation, I focused on two main dimensions: technical competence itself and general interview strategies.
Nvidia’s coding interviews tend to emphasize two key areas. The first is system design. Candidates may be asked to design a platform similar to Uber, with the goal of evaluating their understanding of overall system architecture, data structure choices, and scalability considerations. The second area is programming ability. Nvidia places strong emphasis on solid fundamentals in languages such as C, C++, and Python, as well as knowledge of low-level computer concepts. Questions may also be closely related to Nvidia’s own models or real business scenarios.
To help with preparation, I have also compiled a document containing common Nvidia interview questions. If you are interested, feel free to contact us for access.
One example of an interview question is:
Write an Efficient C Program to Reverse Bits of a Number
In other words, the task is to implement an efficient C program that reverses the binary bits of an integer.
C Language Sample Code
// C code to implement the approach
#include <stdio.h>
// Function to reverse bits of num
unsigned int reverseBits(unsigned int num)
{unsigned int NO_OF_BITS = sizeof(num) * 8;
unsigned int reverse_num = 0;
int i;
for (i = 0; i < NO_OF_BITS; i++)
{if ((num & (1 << i)))
reverse_num |= 1 << ((NO_OF_BITS - 1) - i);
}
return reverse_num;
}
// Driver code
int main()
{
unsigned int x = 2;
printf("%u", reverseBits(x));
getchar();}
Candidates are allowed to use different languages such as C or C++ to solve the problem. For this type of question, Nvidia is not only concerned with whether the final answer is correct, but also with the candidate’s understanding of bitwise operations, loop boundaries, and overall code rigor.
In practice, Nvidia often asks seemingly basic coding problems that require very careful implementation. For example, reversing the bits of an integer may appear simple at first glance, but it places high demands on bit manipulation, boundary handling, and code correctness. Multiple languages are accepted, and the focus is not on the language itself, but on the underlying logic and implementation quality.
Overall, Nvidia interviews strongly emphasize solid fundamentals, a genuine understanding of systems and low-level principles, and strong engineering thinking, rather than just the ability to solve problems by rote practice.
