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Search in Rotated Sorted Array at NVIDIA — Interview Guide

How to approach Search in Rotated Sorted Array in a NVIDIA interview: the optimal approach, what NVIDIA interviewers look for, and company-specific tips.

Updated 2026-09MediumBinary Search

The problem at NVIDIA

Search for a target value in an array that was sorted then rotated at an unknown pivot, in O(log n) time.

NVIDIA uses this problem to assess systems, performance, and parallel thinking. Expect NVIDIA-specific follow-ups that tie the solution back to production reasoning and the company's engineering bar.

Key concepts

At each mid, one half is always sorted; identify it by comparing nums[mid] with nums[left].
If the target lies in the sorted half, search there; otherwise search the other half.
Handle duplicates carefully — when nums[left] == nums[mid] you cannot tell which side is sorted.

Approach

def search(nums, target):
    l, r = 0, len(nums) - 1
    while l <= r:
        m = (l + r) // 2
        if nums[m] == target: return m
        if nums[l] <= nums[m]:
            if nums[l] <= target < nums[m]: r = m - 1
            else: l = m + 1
        else:
            if nums[m] < target <= nums[r]: l = m + 1
            else: r = m - 1
    return -1

What NVIDIA looks for

Low-level fluency — memory, caches, concurrency, SIMD.
Performance mindset — profile, optimize, and reason about bottlenecks.
Domain depth for the team (graphics, ML infra, autonomous systems).
Collaboration across hardware and software boundaries.

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