Performance is the product. Expect memory hierarchy, parallelism and profiling questions that most web-stack preparation never touches.
NVIDIA loops are performance loops. The recurring question is not whether your code is correct but why it is slow, and the expected vocabulary is cache lines, memory bandwidth, branch divergence, occupancy and coalesced access. Candidates whose experience is entirely in managed languages and web services usually find this the largest preparation gap of any big-tech loop.
For GPU-facing roles, CUDA specifics matter: the thread and block model, shared memory, warp behaviour and why a kernel that looks parallel is serialised in practice. For systems roles, C and C++ depth, lock-free structures and profiling methodology carry the same weight. Either way, saying "I would profile it" is only a starting point - you will be asked what you would measure and what you would conclude.
45-60 min
Algorithms plus language-level depth, usually C or C++.
60 min
Memory behaviour, pointers, concurrency and undefined behaviour traps.
60 min
Why is this slow? Cache behaviour, bandwidth limits, parallelisation strategy.
60 min
CUDA, graphics, compilers or ML infrastructure depending on the team.
45 min
Debugging war stories and deep technical ownership.
No - many roles are systems, compiler, driver or infrastructure work where C and C++ depth and performance reasoning matter more. For GPU compute roles, CUDA specifics are expected and hard to bluff.
You are shown or described slow code and asked why. Strong answers reason about the memory hierarchy and bandwidth before touching the algorithm, name the measurement that would confirm the hypothesis, and state what result would falsify it.
Different rather than harder. The algorithmic bar is comparable, but the low-level depth is much higher, and it is the area web-stack preparation leaves entirely uncovered.
Practise the rounds NVIDIA actually runs, and get scored on where you stand.