Amazon SWE Four-Round VO (Passed): Bracket Nesting Depth + Group Anagrams + Vending Machine OOD

VO1: Behavioral + coding

The interviewer barely smiled and didn't say much. Right after my introduction we went into behavioral questions about deadlines and cross-team collaboration. The follow-ups were very detailed — a memorized STAR template isn't enough; you really need to know your stories inside out.

Coding: given a string with parentheses and square brackets, find the maximum nesting depth.

Approach: one pass — increment the depth on an opening bracket and update the maximum, decrement on a closing bracket, and return the maximum.

Follow-up: what if there's only one bracket type, or several mixed together? The same approach works.

def max_nesting_depth(s):
    depth = best = 0
    for c in s:
        if c in "([":
            depth += 1
            best = max(best, depth)
        elif c in ")]":
            depth -= 1
    return best

VO2: Behavioral + coding

Behavioral: mostly how I make decisions with incomplete information, and how I handle disagreements with peers or my manager.

Coding: group words that are anagrams of each other — without using the sorted-string signature.

Approach: use a 26-length count array as the hash key; a single pass groups everything. After writing it I ran a few tests, and we also discussed the pitfalls of using a product of primes as the key: overflow and collisions.

from collections import defaultdict

def group_anagrams(words):
    groups = defaultdict(list)
    for w in words:
        count = [0] * 26
        for ch in w:
            count[ord(ch) - ord("a")] += 1
        groups[tuple(count)].append(w)
    return list(groups.values())

Time O(n · L), where L is the average word length.

VO3: OOD

A very friendly interviewer who smiled the whole time; the conversation was relaxed. After introductions she asked how I've helped teammates solve problems, about teamwork, and how I handle disagreements or conflicts with my manager.

OOD: design a vending machine. The key is abstracting a Coin base class, with each country's currency as a subclass that implements it. This round felt more like a design discussion than an exam.

VO4: Behavioral

1. Why do you want to apply for this position at Amazon?
2. How do you usually handle it when there's disagreement within your team?
3. Can you share a project that best represents your skills and experience?

This round was clearly about team fit.

Behavioral prep tips:

• Tie your stories together with STAR, and back up the results with numbers — how much efficiency improved, how much cost you saved.
• Ideally each story maps to one or two Amazon Leadership Principles; that makes it much easier for the interviewer to sign off.