The most effective way to prepare for an Nvidia interview is to practice real Nvidia interview questions. Wouldn’t it be nice to have a cheatsheet for your Nvidia interview, so you know the answer to questions before they’re asked? Luckily, this cheat sheet exists–Nvidia published it themselves. Nvidia outlines common interview questions, the values they’re looking for, and the interview process on their own site. Nvidia employees and candidates have shared real Nvidia interview questions and the Nvidia interview process all over the internet on sites like Glassdoor and LeetCode. But how do you know which questions will actually be asked? We’ve compiled the most common Nvidia interview questions and answers so you can go into your interview with confidence.
How Not to Answer Nvidia Interview Questions
One of the worst mistakes candidates make is just casually scanning Nvidia interview questions online before their interview. They study questions carefully, but don’t prepare answers–they plan to “wing” their answers. But Nvidia interviewers certainly don’t have such a casual attitude. They’ll carefully note your responses and meticulously review what you shared.
While you shouldn’t memorize interview responses word-for-word, you should have a pitch prepared along with common interview responses and example experiences. And if you’re preparing for an Nvidia interview, don’t just read the questions in this article. Actually write your own response (or modify our example answers), then practice the answers aloud with a friend or in the mirror. You will absolutely be asked some of the Nvidia interview questions in this guide.
Common Nvidia Interview Questions from Their Own Website
Unlike other top tech companies, Nvidia is fairly tight-lipped about their interview process and common questions. They published only one web page outlining their entire interview process. Technical questions will usually be asked over Hackerrank. Behavioral Nvidia interview questions vary by team, but still screen for cultural alignment and experience.
Nvidia hiring is team specific, as confirmed by a real Nvidia interviewer on Blind. That means that each Nvidia team chooses which interview questions to ask and how to conduct interviews. While the hiring process and principles are very similar across teams, specific questions are up to the team members. However, Nvidia team members are usually extremely busy–now more than ever. This means that technical questions will often be copied directly from Leetcode or Hackerank.
Nvidia’s behavioral interview questions assess your alignment with Nvidia’s core values:
While Nvidia interview questions assess complete cultural fit, real Nvidia interviewers say they’re especially looking for these traits:
Although Nvidia doesn’t directly state it on their website like other tech companies, Nvidia almost always asks behavioral questions based on past experiences. They expect you to prepare past experiences to perform well, according to candidates who’ve interviewed at Nvidia. This means STAR interview technique (situation, task, action, result) will be the most critical step to succeeding in any interview. Here are some examples of behavioral questions at Nvidia.
- Tell me about a time you failed.
- Tell me about a time you disagreed with your manager.
- Tell me about a time you faced a tight deadline with limited resources.
These types of questions will come up in almost any interview you will ever have or have had. But since these questions are so common, we won’t analyze them in-depth here. Check out our full guides on answering “tell me about yourself” questions and “what areas do you need to develop further” questions–these simple questions come up in almost every interview. Instead, we want to focus on technical and behavioral interview questions specific to Nvidia.
Behavioral Nvidia Interview Questions
Half or more of your Nvidia interview questions will probably be behavioral. But the most critical behavioral questions will be specific to Nvidia as a company. These questions are designed to assess how you think and relate to Nvidia–their culture, mission, and values. While these questions don’t necessarily require you to know anything about Nvidia, sharing Nvidia-specific information will certainly help you prove your genuine interest and culture fit.
1. Why do you want to work for Nvidia?
This is your chance to demonstrate cultural alignment with Nvidia and show you’ve done your research. While you obviously shouldn’t lie, prepare a thoughtful answer that’s connected to Nvidia’s stated cultural values.
Based on our data, the most effective way to answer is to explain why you’re a good candidate and how you connect to the company mission, values, products, and goals. This is especially important in Nvidia interviews, since cultural fit behavioral questions usually make up half or more of Nvidia interview questions. Check out our full guide to answering “Why do you want to work here” questions because they’ll come up in almost every interview. However, your answer should depend on the company. Nvidia in particular prefers candidates who have an interest in working at Nvidia long-term,
Our answer:
“That’s a great question. I want to work at Nvidia because I feel that our goals align. I have always been passionate about empowering creators with new technology, especially AI, just like how Nvidia empowers people with computing to transform the world’s largest industries.
In my past role as a machine learning engineer at Etsy, I worked on an AI algorithm to double the scale of its “Best of Etsy library”. The library was curated by merchandisers based on an item’s visual appeal–we needed an algorithm to make the results more relevant. I integrated multiple search engine technologies to incorporate relational) and semantic data as well as the word searched. This resulted in a 15% increase in customer satisfaction with “Best of Etsy” library results. My team’s Machine learning work also allowed Etsy to spot listings that violate our handmade policy, increasing discovered and removed violations by 29% in just one quarter.
With my experience working with Etsy AI to deliver the most relevant results, I know I can provide valuable input to the team so that Nvidia can continue to empower and transform industries with AI. In addition, I know that as a company Nvidia values intellectual honesty and speed, which are extremely important to me in my future role. I am looking to join a growing technology company that is disrupting the world with AI technology. While I loved my prior roles, I want to be more directly involved in AI disruption and pioneering–Nvidia is the perfect place for my skills.”
2. Tell me about a time when you had to work under pressure or a tight deadline.
This is another open-ended experience based question that should be answered with STAR. However, “deadline” and “pressure” should immediately make you think about Nvidia’s cultural value of speed and agility. Based on candidate experiences, Nvidia also emphasizes being an independent self-starter, while communicating as “one team”. We recommend sharing a situation from a previous work experience that demonstrates you working quickly with initiative.
Our answer:
“Nvidia values speed and so do I. As a UX intern at Roblox, I had a tight 2 week deadline to revamp the UX for the homepage to highlight Roblox’s live streaming capabilities. Looking to the future, I wanted to make sure that users were consistently shown new and exciting live streams going forward. So I created a “today’s picks” of live streams that displayed popular live streams based on the user’s previous viewing history. After meeting with the dev team, I also increased the size of livestream thumbnails to 16:9 to make them the most prominent on the home page. By moving quickly and working cross functionally, I was able to finish in just 10 days. Our homepage revamp increased livestream views from the homepage by 62% and added almost a thousand hours of watchtime.”
3. How do you adapt to new technologies and trends in your field?
This question is assessing your alignment with Nvidia’s “adaptability” value. While it can seem like a simple one-sentence response, we still recommend using the STAR method and sharing a past experience. This makes your answer more concrete and allows you to prove your impact and familiarity with Nvidia.
Our answer:
“The machine learning field is evolving at a fast pace–I’ve always found it exciting. When I was working at Etsy, AWS Machine Learning became increasingly popular. My manager asked that my team switch to Amazon SageMaker to develop and deploy machine learning models. Thankfully, I’d already heard of the tool because I was a subscriber to a daily AI newsletter. After my manager’s announcement, I took a few hours each day for the first month to practice the tool after work. I quickly became the office expert on AWS, supporting my teammates with the transition. This resulted in significantly faster deployment. Like Nvidia, I think adaptability is critical, especially in AI. I’m committed to proactive continual learning to adapt to whatever technology has the most impact in the field.”
You can use the same STAR method to answer these other Nvidia interview questions. Since questions are team specific, they’ll generally be the same 25 common interview questions that come up in almost every interview at top companies.
Technical Nvidia Interview Questions from LeetCode
Like Amazon and Netflix, Nvidia interview questions are team-specific. Nvidia team members choose specific interview questions themselves. However, team members are very busy. They’ll often ask questions straight from Leetcode.
Last year, a GitHub user compiled a list of Leetcode Nvidia interview questions directly from Leetcode. For questions with context, Redditor recently shared their experience with Lettcode-style Nvidia interview questions. We still recommend paying for Leetcode to practice for technical interview questions at Nvidia. But if you can’t pay for Leetcode, you can use the Github list and Reddit posts. We’ve also included some of the most common LeetCode style Nvidia interview questions below.
- Last stone weight (one of Nvidia’s favorite questions)
- Reverse Linked List
- Search in Rotated Sorted Array
- Rectangle Area
- LRU Cache
- Rotate Image
- Power of Two
- Number of Islands
- Design HashMap
- Minimum Area Rectangle
- Binary Tree Right Side View
Technical Nvidia Interview Questions for Software Engineers
For software engineers, Nvidia interview questions are usually medium difficulty leetcode questions. However, time is limited. You may only have 45 minutes to solve 3 medium-difficulty Leetcode challenges. Beside Leetcode questions, here are some of the most common Nvidia interview questions asked in software engineering interviews.
- Count the number of bits in an integer (fast solution).
- C++ coding questions
- Knowledge of data structures and algorithms.
- How do polymorphism, encapsulation, and inheritance work?
- What are the differences between C and C++?
- How does static work?
- What are the types of casting in C++?
- How does memory work in a C++ program?
- What is the use of stack and queue in OS?
- What is stack overflow?
- Concurrency related questions.
- Random number generation techniques.
- Find all the possible combinations of letters.
- Writing stack/queue data structure using class, specify methods, attributes, etc.
- Strings reversal using recursion, explain recursion pros and cons.
- How would you go about trying to implement video segmentation using neural networks?
- Create a synchronization barrier for a multithreaded task.
- What is bus arbitration and how do you optimize switch daemon?
- What are virtual functions and why do we need them?
- How do you process an image in CUDA?
- How do you map threads? Is it memory-bound or compute-bound?
- How do you use semaphores?
Nvidia Interview Questions for Data Scientists and Machine Learning
Data science Nvidia interview questions usually include Leetcode medium-difficulty challenges. Expect questions on machine learning and queries. Beside Leetcode questions, here are some other common Nvidia interview questions for data scientists.
- Provide a summary of a project you have worked on with big data.
- Build a recommendation system, from beginning to end.
- Find an anomaly within a time series dataset.
- Write an equation for linear regression.
- Explain how a decision tree works.
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