NVIDIA Corporation is a tech giant known for designing graphic processing units for professional and gaming markets. The company also creates system-on-chip units for car and mobile computing markets. It counts over 18,000 employees, and that number keeps increasing. That’s why if you want to become a part of the NVIDIA corporation, you have to pass through a detailed recruitment process where you’ll have to showcase knowledge, experience, and motives. Read on for the Nvidia interview questions.
Becoming a part of this globally known tech company won’t be easy, but if you know what some of the Nvidia interview questions for data science roles are, you will have the edge over the competition. This guide will help you with the following:
- What Does the Data Science Role at NVIDIA Entail
- How Does the Whole NVIDIA Interview Process Go
- How to Ace HR Round of Questions
- Will You Talk About Real-Life NVIDIA Problem in Technical Round
- What Are the Most Common NVIDIA Interview Questions for the Data Science Position
- What Are the Benefits of Becoming an NVIDIA Employee
What Does the Data Science Role at NVIDIA Entail
When you are looking for employment, it is mandatory to know what that position actually entails. Since you are applying for work in an international company such as NVIDIA, data scientists’ roles will vary depending on many specific terms, features, and products.
Generally speaking, this role at NVIDIA can extend across a wide scope of data science concepts. However, it is, for the most part, focused on deep learning (DL) and machine learning (ML). With this in mind, candidates applying for a data science position should have in-depth knowledge of developing cloud computing cluster solutions. Also, they should know how to deploy machine learning and deep learning models at scale.
This brings us to the required skills that NVIDIA looks for in a candidate. If you are applying for a data science role, you should fulfill these requirements:
- Master or Ph.D. in Data or Computer Science, Electrical/Computer Engineering, or other engineering fields.
- No less than three years (more than eight if you are applying for a senior-level position) of work experience or research with C++ or Python.
- Wide-ranging experience with Machine learning and deep learning algorithms with frameworks like Spark, XGboost, and TensorFlow.
- Knowledge of Python, Java, C++ SQL, or C programming languages
- You should be able to build ETL pipelines in a cloud environment
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There Are Different Data Science Roles
You should know that NVIDIA doesn’t have a dedicated data science department, but there are a number of teams working in the company, each with a different task. There is a team working on RAPIDS, also a team working on data centers, software development crew, and AI-driven auto group.
The responsibilities of data scientists can go from being a deep learning engineer to working as a research scientist with a main focus on computer vision. However, no matter what team you are in, your basic responsibilities will include:
- Developing and demonstrating solutions based on ML/DL to customers
- Performing in-depth study and optimization that will guarantee the best performance on GPU architecture systems.
- Applying DL solutions to segmentation, sequence prediction, memory networks, graph compilers, and more.
- You will have to collaborate with industry partners to provide machine learning solutions to their products.
How Does the Whole NVIDIA Interview Process Go
The duration of the interview process will depend on the position you are applying for. However, most take up to four weeks. As in most tech companies, when you apply for a job, the recruiting process starts with a phone call. The initial phone screen with a recruiter is followed by a phone conversation with a team manager. If there is a need, you will also have a technical phone screen, where you will talk with an engineer.
The phone screens are followed by an onsite interview composed of more than a few one-on-one interviews; each of them will last somewhere between 30 and 60 minutes. This is where you will talk with hiring and product managers and group members. Through these steps, you will be able to showcase your knowledge, experience, and skills, convincing managers that you are the best candidate possible.
How to Ace HR Round of NVIDIA Interview Questions
You applied for a data science position at NVIDIA, and after a short time period, you will get a phone call from HR or the hiring manager. This is the first step in your journey. The resume-based phone interview serves as an exploratory and explanatory part of the selection process. Here you will learn exactly what is expected of you, but also an HR or a manager will go through your resume and relevant projects to determine if you will fit the team.
If you want to impress the recruiters, you should have genuine answers to the questions they ask. Usually, the most common things HR wants to know about you are:
Could you tell me about yourself? Even though this seems like a personal question, don’t go into details talking about your favorite music or movies. Stick to the work experiences and how they can relate to the job opening in NVIDIA. Talk about your current role. Tell the recruiter about your past experiences. Say a bit about the things you are looking forward to in the future. Be honest, and don’t go into too many details.
Could you tell me something about your family? This is where recruiters want to get to know your background, see if you are the breadwinner of your house, and understand how much you need the job.
What about your goals? Most of the time, if you didn’t give a too elaborate answer to the question, could you tell me about yourself, interviewers will ask you about your goals. This is where you can talk about your career plans and wishes. To sound more confident and prepared, make sure you research NVIDIA beforehand. Look into all the ways you can climb the company’s career ladder, and this is how you will show the recruiter you are here for the long run.
Where and how you see yourself five years from now? This might be a follow-up question after the one about your goals. Make sure you reflect on your interest and how they could evolve while you are working as a data scientist.
What are your weaknesses? How about your strengths? Questions about your weaknesses and strengths can be tricky to answer. You don’t want to down talk yourself, but also, you don’t want to come off as arrogant. When talking about weaknesses, you should mention those that are not critical to your job, such as fear of public speaking, being sensitive, bad at one particular foreign language. On the other hand, when talking about strengths, you could make a list beforehand. You can mention you are trustworthy, disciplined, patient, determined. Make sure that the strengths can be beneficial to the job you are applying to.
Questions from your resume. Be aware of what you wrote down in your resume because managers sometimes like to go through it and ask about projects or courses you completed five or ten years ago. It would come off as unprofessional if you didn’t remember them. The solution would be to print out your resume and have it in front of you while talking to the HR recruiter.
Questions from past projects. Since you will be asked to submit your resume and your portfolio when applying for work at NVIDIA, you should refresh your memory on some details from big projects. HR recruiters might ask you to explain to them what was your role in those projects and what exactly did you do.
Will You Talk About Real-Life NVIDIA Problem in Technical Round
After you ace the first round of phone interviews with HR or a project manager, a technical interview will be scheduled. This is where you will talk with a data scientist. The technical round of the interview lasts somewhere between 45 and 60 minutes. It consists of questions related to a real-life NVIDIA problem. Here you will get to explain the machine learning experience and speak about all the ways you might design a DL or ML system and scale the process.
What Are the Most Common NVIDIA Interview Questions for the Data Science Position
The last stage of the interview process is the onsite assessment. This is where you will partake in seven rounds of interviews that can be one-on-one or panel meetings. You will meet up with team members, project, and team manager. Each of these interviews will last somewhere between 45 and 60 minutes.
The insight part of the interview process is a combination of various data science concepts. That’s why it includes software engineering, data analytics, ML, as well as making sure you can fit in within NVIDIA’s core culture and values.
You will be provided with a laptop for the technical part, and you will be expected to perform a coding exercise. Sometimes candidates are asked to perform a coding exercise on a whiteboard as well. Questions in this round can span across advanced statistical concepts to things like deep learning implementation. Suppose you want to prepare for the interview practice coding in languages like Python. Also, freshen up your knowledge of Tensorflow and Keras.
Since there is no way to tell what recruiters will ask you exactly, we compiled a list of the most common NVIDIA interview questions asked when applying for the data science position. You should check them out before the interview because they will give you an edge on the competition:
- You will have to explain how you would detect an anomaly with a given time series dataset.
- What is the distinction between a False and True Positive?
- You will be asked to implement gradient descent in Tensorflow
- You will have to write down the equation for linear regression.
- You will be asked to create a recommendation engine from end to end, from a dataset to deployment in manufacturing.
- You will be asked to explain how a decision tree works under the hood.
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What Are the Benefits of Becoming an NVIDIA Employee
NVIDIA is a company that attracts and retains some of the world’s most talented people. NVIDIA offers its employees a challenging workspace and satisfying work environment, where they can work with experts, create and develop ideas.
When you land a job here, you will get the chance to work alongside some of the top experts in the data science field. On top of that, there are many extra benefits like wellness programs, healthcare coverage, income protection, and compensation programs. Don’t hesitate to apply for a job opening, because even though the recruitment process can be long, in the end, it will all be worth the trouble when you land a dream job.
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