Glassdoor users rated their interview experience at NVIDIA as 100% positive with a difficulty rating score of 3 out of 5 (where 5 is the highest level of difficulty). Candidates interviewing for Sr. Machine Learning Engineer and rated their interviews as the hardest, whereas interviews for Sr. Machine Learning Engineer and roles were rated as the easiest.
The hiring process at NVIDIA takes an average of 21 days when considering 1 user submitted interviews across all job titles. Candidates applying for Sr. Machine Learning Engineer had the quickest hiring process (on average 21 days), whereas Sr. Machine Learning Engineer roles had the slowest hiring process (on average 21 days).
Here are the most commonly searched roles for interview reports -
I applied online. The process took 3 months. I interviewed at NVIDIA (Santa Clara, CA) in Jan 2025
Interview
Consisted of three rounds:
1. Resume walkthrough + LC easy - Loved this round, the interviewer was actually interested in my experience.
2. LC Medium - Real bad experience, the interviewer just gave me the question, wasn't even interested. I managed to pass 13/15 test cases.
3. Hiring manager - Standard behavioral questions, good experience!
Interview questions [1]
Question 1
LC easy - was basically API design
LC medium - was frequency counter for each token processed
I applied in-person. The process took 1 week. I interviewed at NVIDIA (Tel Aviv-Yafo) in Aug 2025
Interview
linux:
commands like file permission, commands like cat ls mkdir touch
networks: layer 2 3 tcp ip switvh and a lot of about network
python: book class litcode
it was easy test in hecker rank platform, hour for all the parts of the test
Interview questions [1]
Question 1
linux:
commands like file permission
networks: layer 2 3
python: book class litcode
I applied through college or university. I interviewed at NVIDIA (Neu-Delhi) in Oct 2025
Interview
NVIDIA’s interview process includes online coding assessments, followed by multiple technical interviews focusing on data structures, algorithms, system design, and GPU fundamentals. Candidates also face behavioral and domain-specific questions assessing problem-solving, innovation, and teamwork skills before final selection.