Nvidia Machine Learning Engineer Interview Questions + Guide in 2024

Considered a leader in the artificial intelligence (AI) and machine learning (ML) industries, Nvidia Technologies and Solutions specializes in graphics processing units (GPUs) and chipsets. Nvidia has already seen a massive YoY boost of 279% in data center revenue, reaching $14.51 billion in Q3 Fiscal 2024.

Machine Learning Engineers at Nvidia are responsible for optimizing ML models, integrating them into real-world applications, and optimizing hardware to efficiently handle the algorithms. If you’re a candidate for the Machine Learning Engineer role at Nvidia, you’ll need to have an in-depth understanding of algorithms, ML model concepts, and statistics.

In this article, we’ll give you a brief outline of the interview process, provide you with a few potential questions, and share a few tips to successfully achieve your goal of being a Machine Learning Engineer at Nvidia.

A multi-level interview process at Nvidia will rigorously test your skills in programming languages, algorithms, and existing in a fast-paced team setting.

For the very purpose of easing you into approaching the questions, here we’ve shared the interview process for the Machine Learning Engineer role at Nvidia.

Submitting the Application

You’ll either be encouraged by a hiring manager or find yourself a job post to submit your application through Nvidia’s career portal or designated recruitment platforms.

Ensure that your resume and cover letter highlight your relevant experience, technical skills, and passion for machine learning. Tailor your application to showcase your expertise in areas such as algorithm development, deep learning frameworks, and experience with projects that demonstrate your ability to solve complex problems in the field of artificial intelligence.

Screening Interview

Your interview journey begins with a screening interview conducted by a recruiter. In this 30-minute session, you’ll delve into discussions about your background, experience, and the technical skills essential for the role.

Technical Interview

After succeeding in the screening round, you’ll move to the technical interview. Experienced engineers will evaluate your machine learning skills through discussions on algorithmic complexity, model optimization, and coding challenges. Demonstrate proficiency in deep learning frameworks and problem-solving for real-world applications. You might tackle specific problems faced by ML Engineers and potentially receive a complex take-home assignment.

Behavioral Interview

Upon excelling in the technical interview, you’ll proceed to the behavioral interview with a hiring manager. This stage focuses on evaluating your soft skills and cultural fit within Nvidia’s collaborative environment. Be prepared to discuss your communication abilities, teamwork skills, and alignment with Nvidia’s values and vision.

Offer and Onboarding

After having successfully navigated through the rigorous interview process, you receive an offer to join Nvidia as a Machine Learning Engineer. This offer signifies your technical prowess, problem-solving skills, and potential to drive innovation in the field of artificial intelligence.

Successfully navigating the interview process for the Nvidia Machine Learning Engineer role requires sound technical knowledge, compatibility with the ethos of the company, and the ability to analyze and practically approach real-world machine learning problems.

While you need to practice hundreds of interview questions, here are a few of them to give you a taste of what to expect.

1. What would your current manager say about you? What constructive criticisms might he give?

Through this question, the interviewer will gauge your self-awareness, ability to reflect on feedback, and understanding of your strengths, critical to function as a Machine Learning Engineer.

How to Answer

Reflect on past performance reviews or feedback sessions with your current manager. Identify areas where you excel and areas where you may need improvement. Mention that constructive criticism should be seen as opportunities for growth and learning.

Example

“My current manager would likely describe me as a dedicated and detail-oriented team member who consistently delivers high-quality work. However, one constructive criticism they might give is that I sometimes get overly focused on perfecting the details and may benefit from occasionally stepping back to see the bigger picture.”

2. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

The interviewer will assess your ability to demonstrate initiative, problem-solving skills, and how you handle challenging situations with this question.

How to Answer

Share a specific example of a project where you went above and beyond expectations. Describe the actions you took, the challenges you faced, and how you overcame them to achieve exceptional results.

Example

“In a recent project, I exceeded expectations by implementing a novel machine learning algorithm that significantly improved the accuracy of our predictive models. I conducted thorough research to identify the most suitable approach, collaborated closely with the data engineering team to preprocess and analyze the data, and iteratively refined the algorithm based on experimentation and feedback. Despite encountering unexpected challenges with data quality and computational resources, I remained proactive and adaptable, ultimately delivering a solution that surpassed initial performance benchmarks.”

3. What are you looking for in your next job?

This behavioral question evaluates your career aspirations, goals, and alignment with Nvidia’s values and the role of a Machine Learning Engineer.

How to Answer

Discuss your motivations, career objectives, and what you value in a workplace environment, highlighting aspects that resonate with the company culture and the specific role you’re applying for.

Example

“In my next job, I’m looking for an opportunity to leverage my machine learning expertise to tackle challenging problems and drive impactful solutions. I value a collaborative and innovative work environment where I can continue learning and growing professionally. Additionally, I’m seeking a role that offers opportunities for mentorship, career development, and meaningful contributions to projects that align with my interests and skill set.”

4. Describe a situation where you had to collaborate with a cross-functional team (e.g., software engineers, hardware engineers) to deliver a machine learning solution. How did you ensure effective communication and cooperation?

As a Machine Learning Engineer at Nvidia, you’ll need to communicate with teams in other domains. This question assesses your ability to collaborate across diverse teams and communicate effectively to achieve common goals.

How to Answer

Share a specific example of a project where you collaborated with cross-functional teams. Describe the challenges you faced, the strategies you employed to facilitate communication and cooperation, and the outcomes achieved through effective collaboration.

Example

“In a previous role, I collaborated with software engineers, hardware engineers, and domain experts to develop a machine learning solution for predictive maintenance in industrial equipment. To ensure effective communication and cooperation, I organized regular meetings to align ourselves on project goals, timelines, and technical requirements. I established clear channels of communication, including Slack channels and shared project documentation, to facilitate real-time collaboration and information sharing. Additionally, I encouraged open dialogue and active participation from all team members, leveraging each individual’s expertise to overcome challenges and deliver a robust solution that met stakeholder expectations.”

5. Can you describe a situation where you had to deal with conflicting priorities or feedback from different stakeholders in a machine learning project? How did you resolve it?

You’ll be evaluated for your conflict resolution skills and ability to prioritize tasks and manage stakeholder expectations in Nvidia’s dynamic environment.

How to Answer

Share a specific example of a situation where you encountered conflicting priorities or feedback from stakeholders in a machine learning project. Describe the steps you took to address the conflicts, prioritize tasks effectively, and ensure alignment with project objectives.

Example

“During a machine learning project focused on developing a recommendation system for an e-commerce platform, I encountered conflicting priorities and feedback from different stakeholders regarding feature prioritization and model performance metrics. To resolve these conflicts, I scheduled a series of stakeholder meetings to understand their perspectives and priorities. I facilitated constructive discussions to reach a consensus on key project milestones, balancing technical requirements with business objectives. Additionally, I maintained transparent communication channels and provided regular updates on project progress, soliciting feedback and adjusting our approach as needed to address stakeholder concerns. By actively engaging with stakeholders and fostering collaboration, we successfully delivered a solution that met both technical and business requirements while managing conflicting priorities effectively.”

6. Let’s say you work as a Data Scientist at DoorDash. You are tasked to build a machine learning system that minimizes missing or wrong orders placed on the app. How would you go about designing this system?

Your technical interviewer will assess your ability to design a machine learning system to address a specific problem related to order accuracy in a real-world scenario through this question.

How to Answer

Start by breaking down the problem into smaller components, and discuss the types of data you would collect and the features you would engineer. Explain the importance of selecting appropriate models and how you would evaluate the system’s performance.

Example

“To minimize missing or wrong orders on DoorDash, I would start by collecting relevant data such as order history, user interactions, and restaurant information. Then, I would engineer features such as order frequency, time of day, and location to capture patterns related to order accuracy. For model selection, I would consider using classification algorithms like logistic regression or random forests, which are suitable for predicting binary outcomes like correct or incorrect orders. Finally, I would evaluate the system’s performance using metrics such as accuracy, precision, and recall to ensure its effectiveness in minimizing errors.”

7. Let’s say that you worked as a Machine Learning Engineer at Airbnb. You’re required to build a new dynamic pricing algorithm based on the demand and availability of listings. How would you build a dynamic pricing system? What considerations would have to be made?

This question evaluates your ability to design a machine learning model for a business that has a similar dynamic system to Nvidia.

How to Answer

Outline the key components of a dynamic pricing system. Discuss the types of data you would collect and how you would use this data to engineer features. Explain the importance of modeling techniques like regression or time series analysis to predict optimal prices based on demand and availability. Considerations should include fairness, market dynamics, and user experience.

Example

“For building a dynamic pricing algorithm for Airbnb, I would first collect data on booking history, listing characteristics, and competitor pricing. Then, I would engineer features such as demand forecast, seasonality factors, and listing quality to capture the dynamics of demand and availability. Using regression or time series analysis, I would model the relationship between these features and optimal pricing to maximize revenue while considering fairness and market dynamics. Finally, I would deploy the algorithm in a way that enhances user experience by providing transparent and competitive pricing for Airbnb listings.”

8. How would you design a classifier to predict the optimal moment for a commercial break during a video?

Your ability to design a classifier to determine the best timing for inserting commercial breaks in video content will be assessed through this question.

How to Answer

Start by identifying relevant features for predicting the optimal moment for a commercial break. Discuss potential machine learning algorithms suitable for classification tasks, considering factors like real-time processing and model interpretability. Additionally, address the importance of validation methods to ensure the classifier’s accuracy and effectiveness.

Example

“To design a classifier for predicting the optimal moment for a commercial break during a video, I would consider features such as viewer engagement metrics, content dynamics, and historical data on commercial effectiveness. Machine learning algorithms like decision trees or neural networks could be suitable for this classification task, given their ability to handle complex feature interactions and real-time processing. Validation methods such as cross-validation or A/B testing would be crucial to ensure the classifier’s accuracy and effectiveness in identifying the best timing for commercial breaks.”

9. Consider a stick of length 1. It is broken into three pieces at two randomly selected points. What is the probability that the three pieces can form a triangle?

This question assesses your understanding of probability and geometric constraints in a given scenario, which are critical skills for a Machine Learning Engineer over at Nvidia.

How to Answer

Explain the geometric constraints that must be satisfied for the three pieces to form a triangle. Then, derive the probability using principles of combinatorics and probability theory.

Example

“To determine the probability of forming a triangle with a stick of length 1 broken into three pieces at random points, we must consider the geometric constraints dictated by the triangle inequality theorem. The sum of the lengths of any two sides of a triangle must be greater than the length of the third side. By applying principles of combinatorics, we can calculate the probability that the randomly chosen points satisfy these constraints and thus form a triangle.”

10. What are MLE and MAP? What is the difference between the two?

The interviewer, with this question, seeks to evaluate your understanding of statistical estimation methods commonly used by machine learning engineers at Nvidia.

How to Answer

Define MLE and MAP estimation, explaining their differences in terms of objectives and assumptions. Discuss scenarios where each method is typically used and how they relate to Bayesian and frequentist approaches to parameter estimation.

Example

“Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a model by maximizing the likelihood function given the observed data. In contrast, Maximum A Posteriori (MAP) estimation incorporates prior knowledge or beliefs about the parameters into the estimation process by maximizing the posterior probability given the data. While MLE aims to find the parameter values that best explain the observed data, MAP estimation balances the observed data with prior information, making it more robust in cases of limited data. MLE is commonly used in frequentist statistics, whereas MAP estimation is a key concept in Bayesian inference, where prior beliefs are explicitly taken into account.”

11. What’s the difference between Lasso and Ridge Regression?

Your understanding of different regularization techniques in regression and their respective impacts on model coefficients will be assessed with this question.

How to Answer

Start by explaining the basic concepts of Lasso and Ridge Regression, focusing on how they add penalty terms to the loss function to prevent overfitting. Then, highlight the key difference between them in terms of the type of penalty and how it affects the coefficients of the features. Finally, discuss scenarios where one might be preferred over the other based on the sparsity of features or the need for feature selection.

Example

“Lasso Regression adds a penalty term based on the absolute values of the coefficients (L1 norm), leading to sparsity in the model and potentially setting coefficients to zero, which acts as a form of feature selection. On the other hand, Ridge Regression adds a penalty term based on the square of the coefficients (L2 norm), which shrinks the coefficients towards zero without necessarily setting them exactly to zero. In practice, Lasso is preferred when there are many irrelevant features and feature selection is desired, while Ridge is preferred when multicollinearity is a concern and all features are potentially relevant.”

12. We have two models: one with 85% accuracy and one with 82%. Which one do you pick?

This question evaluates your ability to make decisions based on model performance metrics. You’ll often face these kinds of conflicts as a Machine Learning Engineer at Nvidia.

How to Answer

Explain your approach to evaluating model performance beyond just accuracy, considering metrics like precision, recall, F1 score, etc. Discuss the trade-offs between different metrics and how they align with the specific goals of the project or application. Consider factors like the importance of false positives and false negatives in the context of the problem domain.

Example

“While accuracy is important, it’s essential to consider other performance metrics like precision, recall, and F1 score, especially in scenarios where false positives or false negatives carry different costs. I would assess the business requirements and implications of each model’s performance, considering factors such as the application domain, user expectations, and potential consequences of misclassifications. Ultimately, I would choose the model that best aligns with the project’s goals and priorities.”

13. How would you optimize a machine learning model for deployment on NVIDIA’s GPUs, considering factors like memory bandwidth, parallelism, and computational efficiency?

The interviewer will evaluate your understanding of optimizing machine learning models for GPU-accelerated platforms and stakeholders for Nvidia products.

How to Answer

Begin by discussing the advantages of GPU acceleration in terms of parallelism and computational power. Then, elaborate on techniques for optimizing models for GPU deployment and optimizing algorithms for GPU architectures. Mention specific tools or libraries that facilitate GPU optimization, and provide examples of optimizations you’ve implemented in past projects.

Example

“When optimizing machine learning models for deployment on NVIDIA’s GPUs, it’s crucial to leverage the parallel processing capabilities and computational efficiency offered by these platforms. This involves minimizing memory bandwidth usage by optimizing data access patterns and reducing unnecessary memory transfers between CPU and GPU. Additionally, exploiting parallelism through techniques like batch processing, kernel fusion, and asynchronous execution can significantly improve performance. It’s also important to optimize algorithms for the architecture of NVIDIA GPUs, utilizing features like tensor cores and CUDA libraries for efficient computation. For example, I’ve previously optimized deep learning models by utilizing mixed-precision training and model pruning to reduce memory footprint and improve computational efficiency on GPU-accelerated platforms.”

14. Discuss the importance of data preprocessing and feature engineering in developing robust machine learning models, especially when working with NVIDIA’s GPU-accelerated platforms.

This question assesses your understanding of the role of data preprocessing and feature engineering in developing robust machine learning models, particularly in the context of GPU-accelerated platforms like Nvidia.

How to Answer

Explain the importance of data preprocessing in preparing raw data for modeling. Discuss the significance of feature engineering in creating informative features that capture relevant patterns in the data. Provide examples of preprocessing and feature engineering techniques that are well-suited for GPU deployment.

Example

“Data preprocessing and feature engineering play a critical role in developing robust machine learning models, especially when deploying on NVIDIA’s GPU-accelerated platforms. Data preprocessing involves tasks like cleaning, normalization, and handling missing values, which are essential for ensuring data quality and consistency before modeling. Feature engineering, on the other hand, involves creating informative features that capture relevant patterns in the data and can significantly impact model performance. By optimizing data preprocessing and feature engineering pipelines, we can reduce computational overhead and memory usage, leading to improved scalability and efficiency on GPU-accelerated platforms. For example, techniques like feature scaling, dimensionality reduction, and data augmentation can enhance the performance of deep learning models on NVIDIA GPUs by reducing training time and memory footprint.”

15. Can you discuss the trade-offs between model interpretability and complexity, especially in the context of NVIDIA’s deep learning frameworks like TensorFlow and PyTorch?

The interviewer seeks to assess your understanding of the trade-offs between model interpretability and complexity, specifically in the context of NVIDIA’s deep learning frameworks like TensorFlow and PyTorch.

How to Answer

Begin by explaining the importance of model interpretability in understanding model predictions and gaining insights into underlying patterns in the data. Discuss how deep learning models can sacrifice interpretability for improved performance and predictive accuracy. Discuss strategies for balancing model complexity with interpretability.

Example

“In the context of NVIDIA’s deep learning frameworks like TensorFlow and PyTorch, there’s often a trade-off between model interpretability and complexity. While complex deep learning models can achieve high predictive accuracy, they may lack interpretability, making it challenging to understand how decisions are made and interpret the underlying patterns in the data. This can pose challenges in gaining trust, ensuring transparency, and meeting regulatory requirements. However, there are strategies for balancing model complexity with interpretability, such as using simpler architectures, incorporating interpretability techniques like attention mechanisms or layer-wise relevance propagation, and validating model decisions with domain experts. By carefully considering these trade-offs and selecting appropriate interpretability techniques, we can develop deep learning models that strike the right balance between complexity and interpretability, ensuring both high performance and explainability.”

16. Can you discuss the advantages and limitations of gradient-based optimization algorithms like Adam or RMSprop, especially when applied to large-scale machine learning tasks on NVIDIA’s hardware?

Your understanding of optimization algorithms commonly used in deep learning and their performance on NVIDIA hardware are assessed through this question.

How to Answer

Briefly explain the advantages and limitations of gradient-based optimization algorithms like Adam or RMSprop, particularly when applied to large-scale machine learning tasks on NVIDIA hardware.

Example

“Adam and RMSprop offer advantages such as fast convergence and adaptive learning rates, which are beneficial for training large-scale machine learning models on NVIDIA hardware. However, they also have limitations such as sensitivity to hyperparameters and high memory usage, which can impact their performance on GPU-accelerated tasks.”

17. Describe the role of regularization techniques (e.g., L1, L2 regularization) in preventing overfitting in machine learning models and how they can be adapted for NVIDIA’s GPU-accelerated training.

The interviewer will evaluate your understanding of regularization techniques and their implementation on NVIDIA’s GPU-accelerated training through this question.

How to Answer

Explain the role of regularization techniques in preventing overfitting in machine learning models. Discuss how these techniques can be adapted for NVIDIA’s GPU-accelerated training, considering factors like parallel processing capabilities and memory optimization.

Example

“Regularization techniques such as L1 and L2 regularization help prevent overfitting in machine learning models by penalizing large weights. When adapted for GPU-accelerated training on NVIDIA hardware, these techniques can leverage parallel processing capabilities for faster computations and memory optimization, thereby improving the efficiency of training deep learning models.”

18. Explain the concept of ensemble learning and how it can be implemented to improve model performance on NVIDIA’s hardware platforms.

This question tests your understanding of ensemble learning and its application on NVIDIA hardware platforms.

How to Answer

Define ensemble learning and explain how it combines multiple models to improve predictive performance. Discuss how ensemble learning can be implemented on NVIDIA’s hardware platforms, leveraging features like parallel processing and memory optimization for efficient model training.

Example

“Ensemble learning combines multiple models to improve predictive performance by reducing variance and bias. On NVIDIA’s hardware platforms, ensemble learning can be implemented by training individual models in parallel and combining their predictions efficiently. This approach utilizes GPU acceleration and memory optimization to enhance model performance.”

19. How do you approach model evaluation and validation in a machine learning project, particularly when dealing with large datasets?

Your approach towards model evaluation and validation in the context of large datasets will be assessed by the interviewer with this question. Working with large datasets and model evaluation is critical as an MLE at Nvidia.

How to Answer

Describe your approach to model evaluation and validation, emphasizing techniques such as cross-validation, train-test splits, and performance metrics selection. Discuss strategies for handling large datasets efficiently during evaluation and validation, including data preprocessing and parallel processing.

Example

“In a machine learning project, I approach model evaluation and validation by employing techniques such as cross-validation and train-test splits to assess performance robustness. When dealing with large datasets, I implement data preprocessing steps to manage memory efficiently and leverage parallel processing capabilities on platforms like NVIDIA GPUs for faster evaluation and validation.”

20. How do you handle memory management and resource allocation in Python applications, especially when dealing with large datasets or complex deep-learning models?

Training deep-learning models through large datasets will be one of your responsibilities as a Machine Learning Engineer at Nvidia. Your understanding of memory management and resource allocation in Python applications, particularly in the context of large datasets or complex deep-learning models, will be evaluated through this question.

How to Answer

Explain strategies for memory management and resource allocation in Python applications, focusing on techniques like data streaming, batching, and memory profiling. Discuss specific considerations for handling large datasets or complex deep-learning models, including GPU memory utilization and optimization.

Example

“In Python applications, I handle memory management and resource allocation by implementing data streaming and batching techniques to minimize memory usage. For large datasets or complex deep-learning models, I conduct memory profiling to identify memory-intensive operations and optimize GPU memory utilization. Additionally, I leverage tools like NVIDIA’s CUDA memory management APIs for efficient resource allocation on GPU-accelerated platforms.”

Here is a brief guideline on how to prepare for the Machine Learning Engineer role at Nvidia, as extracted from the successful candidate experiences.

Technical Preparation

The technical interview is an integral part of the Machine Learning Engineer interview at Nvidia. Here are the areas that you need to focus your preparation time on:

• Mathematics Fundamentals: Ensure a strong understanding of linear algebra, calculus, probability, and statistics. Topics like matrix operations, derivatives, integrals, probability distributions, and hypothesis testing are crucial.
• Machine Learning Algorithms: Have a solid grasp of various machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, etc. Consider learning from our Modeling and ML Learning Path.
• Deep Learning: Familiarize yourself with deep learning concepts, including artificial neural networks, Machine Learning System Design, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their architectures.
• Programming Skills: Proficiency in programming languages commonly used in machine learning, such as Python, R, or Julia. Additionally, knowledge of libraries like NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch is essential.
• Data Preprocessing and Feature Engineering: Practice our coding challenges to grow more skilled in data cleaning, handling missing values, normalization, standardization, feature scaling, and feature extraction. Also, consider practicing with take-homes to further evaluate your technical proficiency.

Non-Technical Preparation

As essential are technical skills for the Nvidia machine learning interview, non-technical preparation also are quite critical to your success. Here’s how you should dedicate yourself to this part of your preparation:

• Problem-Solving Skills: Develop strong problem-solving skills to approach real-world challenges with creativity and critical thinking. Consider solving interview questions to gain an edge over other candidates.
• Communication Skills: Practice effectively communicating complex technical concepts to both technical and non-technical stakeholders, including team members, managers, and clients. Consider participating in IQ Mock Interviews to refine your approach and help others do the same.
• Domain Knowledge: Gain domain-specific knowledge relevant to the industry or sector you’re interested in applying machine learning techniques to. Understanding the business context and domain-specific challenges is crucial for delivering effective solutions.

Additional Tips

Here are a few more tips to approach your interview with confidence:

• Build a Strong Portfolio: Work on personal projects, participate in hackathons, contribute to open-source projects, or seek internships to build a robust portfolio showcasing your machine learning skills and experience. You may also access the machine learning datasets and project ideas offered by our IQ team.
• Collaborate and Network: Collaborate with peers on machine learning projects, join online communities, attend meetups, and network with professionals in the field to gain insights, mentorship, and potential job opportunities.

How much do Machine Learning Engineers at Nvidia Make in a year?

The salary for Machine Learning Engineers at Nvidia can vary based on factors such as location, experience, and specific job roles. On average, Machine Learning Engineers at Nvidia are well-compensated, with salaries ranging from $111K to $262K. The average total compensation, however, even clocks to $419K.

Find more information on the IQ Machine Learning Engineer Salary Guide.

Where can I discuss interview experience with other candidates for the Nvidia Machine Learning Engineer role?

You can discuss interview experiences with other candidates for the Nvidia Machine Learning Engineer role on various online platforms and forums. Interview Query’s Discussion Board has threads dedicated to sharing interview experiences at specific companies, including Nvidia.

Does Interview Query have job postings for the Nvidia Machine Learning Engineer role?

Yes, the Jobs Board hosts recent job postings for different roles at different companies, including the Machine Learning Engineer role at Nvidia.

Hopefully, you’ve found our Nvidia machine learning interview questions resourceful and practical.

We also have a complete Nvidia Interview Guide, where you’ll find interview guides for other job roles, including Data Analyst, Business Analyst, Product Manager, and Software Engineer.

Try focusing on our Machine Learning Interview Questions, algorithms, and Python Machine Learning Questions, as these can help you crack Nvidia’s interview.

To wrap things up, we hope that we’ve assisted you with your interview journey. If you need any help, don’t hesitate to reach out to us. Alternatively, consider joining the IQ community, as we also offer other perks that can help you not only succeed in an interview at Nvidia but also at other companies as well.

Ensure you’ve prepared experience stories for the behavioral interview. All the best!