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 through college or university. The process took 4 weeks. I interviewed at NVIDIA (Tel Aviv) in May 2025
Interview
a big question of a circuit- a capacitor, a resistor and an inverter, I was requested to analyze the circuit. the capacitor was connected to the ground and to the inverter, and the inverter was parallel to the resistor.
Interview questions [1]
Question 1
a big question of a circuit- a capacitor, a resistor and an inverter, I was requested to analyze the circuit. the capacitor was connected to the ground and to the inverter, and the inverter was parallel to the resistor.
I applied through an employee referral. The process took 5 weeks. I interviewed at NVIDIA (Tel Aviv) in Nov 2025
Interview
They were very nice. Asked to write the code but didnt care about syntax. They did want to get to know me. They asked a lot about run time complexity and space complexity.
Interview questions [1]
Question 1
write a data structure that you can implement these function in the most efficient way:
getVal(int index)
setVal(int index, char val)
setAllVal(char val)
Basically some team open discussion on research
The manager and team members directly talk with me on my research, related questions, potential projects
After that, 1 week later got offer
Interview questions [1]
Question 1
Seed-free attack discovery
How can we design a red-teaming agent that, given only a target model and a policy spec (no human seeds), can autonomously discover new classes of failures instead of just rediscovering known jailbreaks?
Objective for the red-teaming agent
What is the “right” objective for an automated red-teamer—maximize violation rate, diversity of failure modes, severity, or some coverage metric over a threat taxonomy—and how do we estimate these online under a fixed query budget?
Search algorithms under a strict budget
Can we use bandits / Bayesian optimization / tree search over prompt templates and mutations to maximize the number of distinct verified failures per 1,000 queries? How would we define and estimate “distinctness”?
Generalization across models and updates
How can we train or adapt a red-teaming agent that transfers across model families and model updates, rather than overfitting to one specific checkpoint?
Closed-loop verification
What is an effective architecture for the verify-loop (judge models, tools, simulators) so that the agent can autonomously filter false positives and refine attacks, without human in the loop?