NVIDIA Software Engineer Interview Guide
Updated by Nvidia candidates
Written by Graham Carlson, Senior Technical ContributorCompared to other companies, NVIDIA’s interview process is extremely team-focused, and almost every question will flow from the current and future projects of the team you’re applying to.
NVIDIA’s SWEs are expected to have deep, granular knowledge of their specific domain, including the full stack down to the OS and kernel.
Their questions will focus on your domain knowledge, your use of NVIDIA’s open-source tools, and your hands-on coding experience. They want to see you build systems from scratch.
As a mature, established company, NVIDIA is less interested in speed and more invested in performance, efficiency, and optimization.
Below, we break down the complete SWE interview process at NVIDIA.
Interview Process
NVIDIA’s SWE interview process will assess your fit for the specific team as much as your general skill level and knowledge. The questions will vary greatly depending on the role and team. Here’s a general outline of how each interview looks:
- Recruiter screen: A quick call that focuses on the team you are applying to and how your background fits in with their work
- Technical screen: A brief discussion about your background, followed by an open-ended coding question and several follow-ups
- Hiring manager interview: A high-level discussion about your background, with a mix of behavioral and technical questions
- Onsite of four interviews, including a coding round, a system design round, a Q&A about your domain knowledge, and a behavioral assessment.
Our guides are built from recent, real, first-hand insights shared by both interviewers and candidates.
Processes change. If your experience differs, tell us here–we actively update our information.
Recruiter screen
This call will focus on the details of the position and the team, as NVIDIA’s approach is to find the best fit for a specific team rather than a company-wide set of criteria.
You’ll also be asked about your experience and any overlap it has with the team’s current and future projects. It’s important that you be able to identify these points of overlap and point them out where relevant.
Recruiter Screen Questions:
What technical contributions have you made to a project?
Asked at Technical screen
In this round, you’ll be paired with an engineer, who will spend ten minutes asking about your background before you dive into a technical challenge.
NVIDIA asks team-specific technical questions that are open-ended and do not have a single solution. This round will also include multiple follow-up questions about your solution.
For example, for an LLM-focused role, you might be asked to design an LRU cache. You would then get questions related to CUDA and multithreading to improve GPU efficiency.
NVIDIA prefers that you be able to build from scratch, rather than relying on a language’s built-in functions and library. They’ll want to see your object-oriented programming skills and your willingness to “get your hands dirty” writing code.
Technical Screen Questions:
Asked at Hiring manager call (30 min)
This is a shorter hybrid interview that combines high-level questions about your past work with behavioral questions to determine your fit for the team.
Each team will have a specific workflow and set of responsibilities, and they’ll want to know that you’ll be able to fit in and work well with their established structure.
Their behavioral questions will focus on the meta-level of your experience, asking you to discuss what you found challenging in prior roles and which workflow you feel suits you best.
They’ll also touch on your skills at a high level, any mentorship experience you’ve had, and examples of technical and behavioral problem-solving.
Hiring Manager Questions:
Asked at Onsite
These interviews may take place virtually or in-person, generally over one day, depending on team availability.
Coding
In contrast to the prior coding screen, this round will focus more on speed and efficiency, as you will be given multiple coding skills questions.
These won’t necessarily be relevant to the specific role you are applying to, but there may be follow-ups asking you to apply your method to a real-life example.
With enough time, they may ask you about the method you chose and walk you through alternatives, trade-offs, or even the history of that approach and how it has changed over time.
They prefer those who can dig into the details and not just follow best practices, but understand why those practices work best and how they were developed.
Common questions will involve you finding the optimal order or deployment of resources to complete an operation in the shortest time possible.
Coding Questions:
Asked at Adobe, Apple, Goldman Sachs + 6 more 
Asked at System Design
This will be domain-specific, and you’ll need to show not only your familiarity with common issues and best practices, but also the specifics of how this system intersects with NVIDIA’s work.
As a large, established company, NVIDIA has extensive proprietary and OS tools for each domain, and incorporating these into your design is critical.
For example, an LLM-focused role might raise questions about managing GPU resources, parallel computing, and working with large, distributed systems.
Even if your system is well-designed, if it lacks awareness and direct experience with NVIDIA’s tools and resources, you might struggle in this round.
System Design Questions:
Domain Knowledge
This will be a question-and-answer format, covering basic and advanced concepts relevant to the team’s domain. This can include recent developments as well as historical concepts.
However, this isn’t just trivia; it's a test of your direct domain knowledge and experience, and the questions will move beyond the conceptual into real-world examples and applications.
For example, an LLM role might ask about agent context windows, while a hardware interview might focus on the history and current state of ray tracing.
Because they are domain-specific, the questions will go deep into the details and will be challenging to answer unless you have extensive experience working in the domain.
Domain Knowledge Questions:
Behavioral
This behavioral round centers around technical topics and examples, rather than high-level concepts like emotional intelligence.
You will be presented with hypothetical situations and a set of constraints, and will have to determine priorities and develop a strategy to secure team buy-in.
When you are prompted for examples, you’ll need to dig into the details of your past projects, why you took the approach you did, and what you learned.
A hypothetical might involve executive pressure to meet an unrealistic hardware efficiency benchmark. How would you explain why it’s unrealistic and push for something more manageable?
Behavioral Questions:
Asked at Common Mistakes
Here are some of the mistakes people make when preparing to interview for an SWE role at NVIDIA:
- Not familiarizing themselves with the specifics of the team they are applying to, including their current projects and any OS contributions they have made
- Only preparing for high-level, strategic questions instead of practical, use-case-driven preparation
- Only studying the current and most popular domain topics, as their domain questions will cover past methodologies and their evolution over time
- Relying too much on libraries and other efficiency tools, as NVIDIA prizes SWEs who are willing to “get their hands dirty” and write everything from scratch
- Preparing with the assumption that NVIDIA hires based on company criteria, as they will be assessing fit for the team, and less fit for the company
- Failing to learn and understand the details of the domain, as they want candidates with knowledge of the whole stack, including the OS, memory use, and kernel
Interview Prep
Study the team and domain: While some companies hire for a specific set of centrally-determined values, NVIDIA hires based on how candidates fit into an existing team. You should familiarize yourself with the work the team is doing, the tools they’re using, and any open-source contributions they’ve made. You should also study the principles and history of the domain, not just the here-and-now.
Understand NVIDIA’s tools and approach: As a large, established company, NVIDIA has significant documentation and resources covering its contributions to each field. Performing well in your interview will require you to demonstrate knowledge of these contributions and how they help solve common problems across LLMs, GPUs, gaming hardware, and other domains.
Get hands-on: You can’t just watch a tutorial or read some documentation and be prepared. You should practice by getting hands-on with the tools and use cases the team deals with. If you’re applying for an LLM role, rent a GPU and learn how to train a model. For a firmware position, you might practice with RTOS, device drivers, and the Linux kernel.
About the Role
NVIDIA SWEs work across many fields, and their responsibilities vary greatly depending on the team. Here are some common characteristics:
Build for scale: NVIDIA is one of the largest tech companies in the world, and its products touch every aspect of modern computing. Even seemingly niche projects could reach millions, so much of your work will involve building for this demand.
Optimization is key: Maximizing the efficiency of their hardware and components is a key element of work at NVIDIA. This has only increased with their heavy investment in the LLM space, which has created enormous demand for compute and energy.
Constant experimentation and research: NVIDIA’s market position is based on its extensive research into every detail of its domain. CUDA is a primary example of this, as it began as a research project and has become a central element of NVIDIA’s strategy.
Core Responsibilities
As above, the details will change based on the team and project, but here are some of the general responsibilities that NVIDIA SWEs have across the organization:
- Collaborate within your team and across teams to uncover and deploy hardware and software dedicated to operations like LLM training and inference, video games, VR, and the supporting hardware.
- Work on projects to improve the efficiency of interactions between hardware and software.
- Experiment and develop new tools and products that address computing, data, and energy constraints. Document and publish findings and contribute to OS projects.
- Set new performance benchmarks and increase the usage and efficiency of current and future hardware.
Compensation
Average total compensation by level for NVIDIA SWEs:
IC1: $160,000
IC2: $197,000
IC3: $324,000
IC4: $348,000
IC5: $436,000
IC6: $610,000
Job Requirements
Experience
Depending on the level, SWEs at NVIDIA are expected to have between 2 and 12 years of experience, with a significant portion of this spent working within the team’s domain.
Each team will have specific coding language and domain expertise requirements. Nearly every team will favor those with experience building highly scalable products and tools.
Education
SWE roles require a BS, MS, or PhD in a relevant topic or equivalent experience.
Resources
- NVIDIA Careers Page
- Culture at NVIDIA
- NVIDIA Developer Blog
- NVIDIA: How we hire
- Software Engineering Interview Prep
- NVIDIA Interview Questions
- Software Engineer Interview Questions
FAQs
How long is the NVIDIA SWE interview process?
NVIDIA’s interview process takes anywhere from 4 to 8 weeks, depending on team availability.
Does NVIDIA have internships?
NVIDIA has programs for new college graduates and students. Internships run year-round and last at least 12 weeks.
Do I need AI or LLM experience to get a job at NVIDIA?
Despite their major investment in the LLM space, NVIDIA also offers products and tools that fall outside this domain, so it isn’t required for every role.
You will need direct, hands-on experience in whatever the team you’re applying to is working on, be it LLM training or autonomous cars.
Does NVIDIA offer remote work opportunities?
Following their team-first approach, NVIDIA’s rules are flexible, allowing each team to decide which approach works best. Some roles and teams are remote-optional, while some may require part- or full-time in-office work.
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