Nvidia
Hardware and AI software leader powering the global generative AI revolution.
4 Rounds
~25 Days
Very Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Machine Learning Engineer
•
Technical
•
medium
Explain the CUDA memory hierarchy. Specifically, compare shared memory, global memory, and constant memory. How do these impact the performance of a custom ML kernel?
#CUDA
#GPU Architecture
#Performance Optimization
Machine Learning Engineer
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Technical
•
medium
What are the trade-offs between FP32, FP16, BF16, and FP8 formats in deep learning?
#Data Types
#Precision
#GPU
Machine Learning Engineer
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Technical
•
hard
Explain the high-level architecture of an Nvidia GPU. What are Streaming Multiprocessors (SMs) and warps?
#GPU
#CUDA
#Hardware
Product Manager
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Technical
•
hard
Explain the difference between memory bandwidth and compute capability. As a PM, how do you prioritize which to improve for the next generation of data center GPUs (e.g., Blackwell)?
#GPU Architecture
#LLM Bottlenecks
#Prioritization
Difficulty Radar
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The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.