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Qube Research & Technologies·Software Engineer·Onsite - Coding / Algorithms·Senior

SeniorPrefer not to say
Apr 2026

Summary

Qube R&T software engineer interview with two fairly distinct problems back to back. The C++ one was the meatier of the two and took up most of the session. Left feeling okay about the coding but not totally sure how my verbal explanations landed.

Questions Asked (2)

Q1

Implement std::flat_map in C++, including a working iterator. Walk through your design choices and write thorough test cases.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This was a lot to unpack.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints of std::flat_map (sorted contiguous storage, logarithmic lookup, iterator invalidation rules). Then outline the design: a sorted vector of key-value pairs, with iterators wrapping vector iterators, and implement core operations (insert, erase, find, begin/end). Finally, discuss trade-offs and write comprehensive tests covering edge cases, iterator validity, and performance.

Pro tip: Emphasize that std::flat_map is not just a sorted vector—it's about cache efficiency and memory locality. Mention that iterators must remain valid after insertions that don't cause reallocation, and that erase invalidates iterators after the erased element, mirroring std::vector semantics.

1. Clarify requirements and constraints

Ask about expected operations, performance guarantees, and iterator invalidation rules. Confirm that flat_map stores elements contiguously and maintains sorted order by key.

2. Design the data structure

Propose using a std::vector of std::pair<Key, Value> sorted by key. Explain that this provides O(log n) lookup via binary search and O(n) insertion/deletion due to shifting elements.

3. Implement the iterator

Define an iterator class that wraps a vector iterator, providing bidirectional iteration. Ensure it supports standard operations (++, --, *, ->, ==, !=) and maintains const-correctness.

4. Implement core operations

Write insert, erase, find, and access operators. For insert, use lower_bound to find position, then insert into vector. For erase, find element and erase from vector. Handle duplicates according to map semantics.

5. Write thorough test cases

Cover empty map, single element, multiple elements, duplicate keys, insert/erase at beginning/middle/end, iterator invalidation after modifications, and comparison with std::map behavior. Include performance tests for large data.

Key Points to Mention

  • Contiguous storage and cache efficiency compared to node-based maps like std::map.
  • Iterator invalidation rules: insert may invalidate all iterators if reallocation occurs; erase invalidates iterators after the erased element.
  • Time complexity: O(log n) for lookup, O(n) for insertion/deletion due to shifting.
  • Use of binary search (std::lower_bound) for efficient lookup and insertion point.
  • Exception safety: strong guarantee for insert if vector reallocation throws, basic guarantee for erase.
  • Comparison with std::map and std::unordered_map: trade-offs in memory, speed, and iterator stability.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Given an array of integers representing individual wealth (which can be negative, zero, or positive), redistribute the total so every person ends up with as equal a wealth as possible. Talk through your approach while you code.

Algorithms & Data Structures
Author's notes

Simpler than the first problem but the negative values thing made me pause for a second.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: compute the total wealth, then distribute it as evenly as possible, handling remainders by giving some people one extra unit. Discuss the mathematical insight that the result depends only on the total sum and the number of people, not on the initial distribution. Then implement a simple O(n) solution that calculates the base share and remainder, and constructs the final array.

Pro tip: Mention that the initial distribution is irrelevant except for the total sum, and that the problem reduces to integer division and remainder distribution. Also, proactively discuss edge cases like negative totals and empty arrays to show thoroughness.

1. Clarify the problem and constraints

Ask whether the redistribution must be in integer amounts, whether the order of the array matters, and what to return (e.g., the new array or just the values). Confirm that negative wealth is allowed and that the total sum can be negative.

2. Compute total wealth and base share

Sum the array to get the total. Compute the base share as total // n (integer division) and the remainder as total % n. Note that in Python, // and % handle negative numbers correctly, but in other languages you may need to adjust.

3. Distribute the remainder

Assign the base share to every person. Then distribute the remainder by adding 1 to the first 'remainder' people (if remainder is positive) or subtracting 1 from the first 'remainder' people (if remainder is negative, depending on language semantics).

4. Implement and test

Write the code, then test with cases: all positive, all negative, mixed, zero sum, and empty array. Verify that the sum of the new array equals the original total and that the values differ by at most 1.

5. Analyze complexity and discuss optimizations

State that the solution is O(n) time and O(n) space (or O(1) extra space if modifying in place). Mention that no sorting is needed, and that the initial distribution does not affect the result.

Key Points to Mention

  • The total sum is the only thing that matters from the original array; the initial distribution is irrelevant.
  • Integer division and modulo operations are key to computing the base share and remainder.
  • Handling negative totals correctly: in some languages, the remainder can be negative, so adjust the distribution accordingly.
  • The final array will have values that differ by at most 1, ensuring fairness.
  • Edge cases: empty array (return empty), single element (return as is), and all zeros.
  • Time complexity is O(n) and space complexity is O(n) for the output array, which is optimal.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.