NVIDIA

Official guidance

NVIDIA designs GPUs and accelerated-computing platforms along with the software that makes them usable: drivers, compilers, CUDA libraries, AI training and inference software, networking, and systems for data centers, autonomous vehicles, robotics and graphics. Software roles range from low-level systems and performance engineering to ML frameworks and AI infrastructure.

Relevant roles: General software engineering, Low-level software systems, Infrastructure / platform engineering, ML engineering, Training / inference infrastructure. Domains: Algorithms, Software engineering, OS & concurrency, ML & applied AI, AI infrastructure, Interview skills.

What the sources establish

NVIDIA’s official How We Hire page documents the shape of the process: phone, video and in-person interviews with the hiring manager, team members and other groups; 30 to 60 minutes per interview; a required in-person onsite before any offer; a possible HackerRank coding exercise for technical roles; and a strict rule that unapproved outside tools such as ChatGPT lead to disqualification. Not established: the technical topics for any role, the number of interviews, the coding language, and how the process differs between teams.

How We Hire (NVIDIA Careers)
Official
NVIDIA. No visible date (page metadata indicates an update on 2026-08-14); read 2026-10-05. Read in full for this page.
Scope: All NVIDIA candidates, with separate notes for full-time roles and interns
Documented by this source
- Candidates meet the hiring manager, team members and employees from other groups by phone, video and in person; several interviews are possible depending on the role
- An onsite, in-person interview at an NVIDIA office is required before a candidate can be considered for an offer
- One-on-one, small-group or panel interviews usually last 30 to 60 minutes each
- Technical candidates may be asked to complete a coding exercise, usually in HackerRank on a whiteboard or on a laptop NVIDIA provides
- Full-time roles typically start with phone interviews followed by virtual or in-person interviews, and some teams vary; interns typically have phone interviews only
- When interviewing with several teams, feedback may be shared across them; an optional 15-minute Insider Chat in the final stage does not affect the decision
- Onsite arrival requires a physical government-issued photo ID, and visitors must be escorted throughout the facility
Outside or AI tools, according to this source: Using unapproved outside tools such as ChatGPT during the interview results in disqualification. Follow the instructions in your own assessment invitation.
- Describes process and logistics only; it lists no technical topics, languages or interview modules.
Open source(opens in a new tab)Link responded, 5 Oct 2026

Our preparation recommendation

This is our suggestion based on the role and the sources above. It is not a statement by NVIDIA.

Our recommendation, not NVIDIA guidance: the page gives process but no topics, so base technical preparation on the job description. For systems and GPU software roles, prioritize C++ fluency and memory-hierarchy and concurrency reasoning; for AI infrastructure roles, add GPU performance, parallelism and inference topics. Practice coding by hand or on an unfamiliar machine without assistants, since the exercise may be on a whiteboard or a provided laptop and unapproved tools disqualify.

Pattern recognition and mixed practiceNot started
Technical roles may include a coding exercise; broad pattern practice prepares you for an unknown problem set.
Coding fluency and standard-library costsNot started
A whiteboard or provided laptop means coding without your usual setup and autocomplete.
C++ fundamentals: lifetime, ownership and undefined behaviorNot started
For systems, driver and GPU software roles where C++ is the working language; check the job description.
CPU caches, locality, coherence and NUMANot started
Memory hierarchy and locality reasoning underpin performance work on CPUs and GPUs.
Locks, condition variables, atomics and memory orderingNot started
Concurrency and synchronization are core competencies for systems software roles.
GPU architecture, memory hierarchy and arithmetic intensityNot started
For GPU, CUDA and AI-infrastructure roles: architecture, memory hierarchy and arithmetic intensity.
Data, tensor and pipeline parallelism and collectivesNot started
For training-infrastructure roles: data, tensor and pipeline parallelism and collectives.
Prefill, decode and KV-cache sizingNot started
For inference and serving roles: prefill, decode and KV-cache sizing.
Project deep divesNot started
You meet the hiring manager and team members, so be ready to explain relevant projects in depth.
Assessment formats, tool policies and logisticsNot started
The onsite requirement, ID rules and the outside-tools ban need advance planning.

Confirm with your recruiter

The interview sequence for this team: phone screens, the number of virtual or onsite interviews, and their formats (one-on-one, small group, panel).
The level and the technical areas each interview covers (for example C++, CUDA, systems, ML, algorithms).
Whether there is a HackerRank exercise, whether it is on a whiteboard or a provided laptop, and which languages are allowed.
Exactly which tools are approved (documentation, IDE features, AI assistants), since unapproved tools lead to disqualification.
Whether any take-home or presentation is required for your role, and its rules.
Onsite logistics: location, travel, photo ID and escort rules.
Whether you will interview with several teams, since feedback may be shared across them.

Limitations of this page

One process page; no NVIDIA source read here describes technical interview content.
Intern processes are described only briefly (typically phone interviews).