NVIDIA Research Scientist Interview Guide Overview

NVIDIA is at the cutting edge of technology, pioneering the fields of AI, accelerated computing, and graphics. Renowned as one of the most desirable employers globally, NVIDIA’s work is transforming industries and impacting society profoundly. If you're creative, autonomous, and thrive on challenges, NVIDIA is the place for you.

As a Research Scientist at NVIDIA, particularly focusing on Deep Learning or AI, you will engage in groundbreaking research aimed at pushing the boundaries of technology, including generative AI, sparsity techniques in neural networks, and efficient deep learning methods. This role involves a blend of research, collaboration, and product development.

In this guide, we'll walk you through NVIDIA's rigorous yet rewarding interview process, from research presentations to in-depth technical discussions. Get ready to showcase your technical prowess and research acumen with us on Interview Query!

Submitting Your Application

The initial step toward joining NVIDIA as a Research Scientist is crafting a compelling application that showcases your technical skills and enthusiasm for the role. Whether approached by a NVIDIA recruiter or applying independently, scrutinize the job description meticulously and tailor your resume to accentuate specific qualifications and experiences.

Tailoring your CV might involve identifying keywords that hiring managers look for and composing a targeted cover letter. Make sure to emphasize relevant skills and reference past projects or research that align with the job requirements.

Recruiter/Hiring Manager Call Screening

If your CV gets shortlisted, a recruiter from NVIDIA’s Talent Acquisition Team will contact you to verify key details such as your experiences and skillset. During this screening process, you may also encounter behavioral questions.

Sometimes, the hiring manager for the Research Scientist role may also participate in this initial screening. This provides an opportunity to understand more about the role and the company while exploring your technical and behavioral fit for the team.

Typically, the recruiter call lasts about 30 minutes.

Technical Virtual Interview

Once you pass the recruiter round, you’ll be invited for a technical screening round, conducted virtually. This part of the interview will focus on your research interests, recent projects, and what you aim to achieve at NVIDIA.

In this stage, you will likely tackle several coding and machine learning-related questions. The session may also include detailed discussions around papers you've written or methods you've proposed. The interviewers may assess your problem-solving skills by diving deep into concepts such as gradient vanishing problems in neural networks and innovative solutions for efficient DL architectures. This interview usually lasts for an hour.

Onsite Interview Rounds

Upon successfully clearing the virtual technical interview, you will advance to the onsite interview rounds. These rounds are held at one of NVIDIA’s offices and can be intense but rewarding.

1. Research Presentation: You will start with a 1-hour job talk where you present your research to the team. This presentation should cover your past research, current interests, and possible future directions.

2. 1-on-1 Interviews: Following your presentation, you will undergo multiple one-on-one interviews. These are typically around six rounds, focusing on different aspects of your research work, technical skills, and how well you can translate this into innovative solutions at NVIDIA. Expect questions about your recent projects, and be prepared for in-depth technical queries and coding challenges.

3. Chat with Hiring Manager: A final discussion with the hiring manager will provide insights into the team’s dynamics and expectations. This session can also be an opportunity for you to ask questions and better understand your potential role at NVIDIA.

Quick Tips For NVIDIA Research Scientist Interviews

To excel in your NVIDIA Research Scientist interviews, consider the following tips:

• Be Thorough with Your Research: Make sure you deeply understand your own research projects and be prepared to discuss them thoroughly. Highlight how your research aligns with NVIDIA’s goals and objectives.
• Brush Up on Machine Learning Fundamentals: Ensure that you are strong in machine learning concepts, particularly those relevant to NVIDIA’s focus areas like efficient DL architectures and neural network sparsity.
• Practice Coding and Problem-Solving: Coding questions are a significant part of the interview. Practice coding problems and mock interviews on Interview Query to refine your problem-solving speed and accuracy.

Typically, interviews at Nvidia vary by role and team, but commonly Research Scientist interviews follow a fairly standardized process across these question topics.

• Write a function list_fifths to return the fifth-largest number from each sublist in numlists. You're given numlists, a list where each element is a list of at least five numbers. Write a function list_fifths that returns a list of the fifth-largest number from each element in numlists. Return the list in ascending order.

• Write a query to get the top five most expensive projects by budget to employee count ratio. We're given two tables: projects and employee_projects. Write a query to get the top five most expensive projects by budget to employee count ratio. Exclude projects with 0 employees. Assume each employee works on only one project.

• Create a function shortest_transformation to find the shortest transformation sequence between two words. You're given two words, begin_word and end_word, which are elements of word_list. Write a function shortest_transformation to find the length of the shortest transformation sequence from begin_word to end_word through the elements of word_list. Only one letter can be changed at a time, and each transformed word must exist in word_list.

• Write a function rotate_matrix to rotate a 2D array by 90 degrees clockwise. Given a 2D array filled with random values, write a function rotate_matrix to rotate the array by 90 degrees in the clockwise direction.

• Calculate the t-value and degrees of freedom for a test comparing product prices in category 9 to other categories. You're managing products for an eCommerce store and think products from category 9 have a lower average price than those in all other categories. Calculate the t-value and degrees of freedom for such a test. You do not need to calculate the p-value of the test.

• What features would you include in a model to predict a no-show for pizza orders? Imagine you run a pizza franchise and face a problem with many no-shows after customers place their orders. What features would you include in a model to predict a no-show?

• How would you justify the complexity of a neural network model to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of building such a model and explain the predictions to non-technical stakeholders?

• What machine learning methods would you use to build a chatbot for FAQs? You want to build a chatbot system for frequently asked questions. Whenever a user writes a question, you want to return the closest answer from a list of FAQs. What are some machine learning methods for building this system?

• How would you determine if a new delivery time estimate model is better than the old one? You want to build a new delivery time estimate model for food delivery. How would you determine if the new model predicts delivery times better than the old model?

• How would you combat overfitting in tree-based classification models? You are training a classification model. How would you combat overfitting when building tree-based models?

• What metrics would you use to determine the value of each marketing channel? Given all the different marketing channels and their respective costs at Mode, a B2B analytics dashboard company, what metrics would you use to evaluate the value of each marketing channel?

• What are type I and type II errors in hypothesis testing? In the context of hypothesis testing, what are type I errors and type II errors? What is the difference between the two? Bonus: Describe the probability of making each type of error mathematically.

• What business health metrics would you track for an e-commerce D2C business selling socks? If you are in charge of an e-commerce D2C business that sells socks, what business health metrics would you care about tracking on a company dashboard?

• How would you analyze transaction data to understand revenue loss in an e-commerce company? An e-commerce company is experiencing a reduction in revenue for the past 12 months. Given transaction data including date of sale, total amount paid, profit margin, quantity, item category, subcategory, marketing source, and discount applied, how would you analyze the dataset to understand where the revenue loss is occurring?

• How would you measure and analyze the success of a new email campaign? Your company has begun a new email campaign. Given tables detailing users' visits to the site and timestamps of when emails were sent, how would you measure the success of this campaign? Write a query to analyze the success of your campaign.

• What is the downside of only using the R-Squared \((R^2)\) value to determine a relationship between two variables? When analyzing how well a model fits the data, what are the limitations of relying solely on the R-Squared \((R^2)\) value to determine the relationship between two variables?

• What are type I and type II errors in hypothesis testing? In the context of hypothesis testing, explain type I errors (false positives) and type II errors (false negatives). What is the difference between the two? Bonus: Describe the probability of making each type of error mathematically.

• What is an unbiased estimator? Define an unbiased estimator and provide an example that a layman can understand.

• How should you handle skewed home price data in a predictive model? If you are building a model to predict real estate home prices and the distribution is skewed to the right, do you need to take any action? If so, what should you do? Bonus: What should you do if the target distribution is heavily skewed to the left?

• How do you calculate the t-value and degrees of freedom for a category price comparison test? If you are managing products for an eCommerce store and believe products from category 9 have a lower average price than those in all other categories, how would you calculate the t-value and degrees of freedom for this test? You do not need to calculate the p-value.

Example:

Input:

products table

| Column| Type | | --- | --- | | id | INTEGER| | name | VARCHAR| |price|DOUBLE| |category_id|INTEGER|

Output

|Column|Type| |---|---| |t_value|DOUBLE| |d_o_f|INTEGER|

What is the average salary for a Research Scientist at Nvidia?

Q: What does the interview process at NVIDIA for a Research Scientist position involve?

A: The interview process typically includes an initial job talk presentation followed by multiple rounds of one-on-one interviews. Candidates discuss their past research projects, future research interests, and perform technical assessments such as coding and machine learning-related questions.

Q: What should I expect during the job talk presentation?

A: The job talk presentation is a 1-hour session where you showcase your research interests and previous work to the team. This is an opportunity to demonstrate your expertise and align your interests with NVIDIA's goals.

Q: What qualifications are needed for a Research Scientist role at NVIDIA?

A: Candidates usually need a PhD or MS degree in computer science, electrical engineering, or a related field, along with at least 3+ years of relevant work experience. Proficiency in Python and C++, experience with neural network pruning and sparsity, and familiarity with DL training frameworks are highly desirable.

Q: How can I prepare for the technical interviews at NVIDIA?

A: To prepare, you should review your research papers, brush up on coding skills, especially in Python and C++, and thoroughly understand machine learning concepts. Practice problem-solving with technical questions similar to those on platforms like Interview Query.

Q: What is the work culture like at NVIDIA?

A: NVIDIA is known for its innovative and collaborative work culture. The company values creativity, autonomy, and hard work. Employees often work on groundbreaking projects in AI, generative AI, and more, contributing to the advancement of technology.

NVIDIA is on the frontier of artificial intelligence, empowering machines to learn, reason, and interact with the world. As a leader in AI and accelerated computing, NVIDIA offers incredible opportunities for those who are passionate, innovative, and ready to push boundaries.

If you're aspiring to join NVIDIA as a Research Scientist, their interview process is designed to comprehensively evaluate your expertise and research interests. Expect in-depth discussions about your past and future research, engaging with both team members and hiring managers. The experience is not just challenging but enriching, characterized by collaborative problem-solving and a supportive environment.

NVIDIA is in search of visionary Research Scientists focused on deep learning, generative AI, and beyond. Key roles involve pioneering research, collaborating with top-tier teams, and transforming breakthroughs into real-world applications. They offer competitive salaries, equity, and a commitment to diversity and inclusion.

For an edge in your preparation, explore our in-depth NVIDIA Interview Guide. At Interview Query, we arm you with the knowledge and confidence to excel in every interview scenario you'll face at NVIDIA.

Dive into our resources and get ready to make your mark at one of the most exciting tech companies in the world. Good luck with your interview!