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).
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It was a great interview experience overall. I was referred by an employee. There were questions mostly on Scientific Computing and my resume projects, which were both technically challenging and very insightful
Interview questions [1]
Question 1
What does it mean for an algorithm to be numerically unstable?
What are the trade-offs between using FP32, FP16, and BF16 precision when training large-scale models on NVIDIA GPUs?When dealing with massive datasets, how do you decide between using sparse matrix representations versus dense ones, and how does this affect memory bandwidth?
I applied online. I interviewed at NVIDIA (Santa Clara, CA) in Nov 2025
Interview
Had one technical interview with the engineer whose team I would be joining. I did not advance, unsure if there was a second round or only one. The interviewer was nice, and helped me out when I was unsure of a question.
Interview questions [1]
Question 1
Asked me about some MOS device characteristics, some IV graph questions, and then details about a SRAM array on my resume.
I applied online. I interviewed at NVIDIA (Santa Clara, CA)
Interview
The first interview was conducted via video call with a Solutions Architect Director. The discussion focused on understanding my background. The interviewer frequently asked follow-up and clarifying questions based on my responses.