Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Meta SWE coding round, one problem the whole time. Looked deceptively simple at first but the subset search angle took me a minute to fully see.

Questions Asked (3)

Given an array of strings, find a subset you can concatenate such that the resulting string has the maximum number of unique characters, with no character appearing more than once across the whole concatenation.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to sort by length and greedily grab words, which is wrong.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a solution using bitmasking to represent character sets, with dynamic programming or backtracking to find the optimal subset. Discuss time and space complexity, and consider trade-offs between different approaches.

Pro tip: Demonstrate awareness of the constraints: if the alphabet is limited (e.g., lowercase letters), bitmasking is efficient; otherwise, discuss alternative strategies. Also, mention that the problem is NP-hard in general, so for large alphabets, heuristics or approximations may be needed.

1. Clarify the problem

Ask about the character set (e.g., ASCII, Unicode), array size, and whether the subset can be empty. Confirm that the concatenation must have no repeated characters.

2. Identify constraints and edge cases

Consider cases like strings with duplicate characters, empty strings, and the maximum possible unique characters. Discuss how these affect the solution.

3. Propose a bitmasking approach

Represent each string's character set as a bitmask. Use DP or backtracking to select a subset of strings whose masks are disjoint and maximize the total number of bits set.

4. Analyze complexity and trade-offs

Explain that the problem is NP-hard (related to maximum set packing) and discuss the complexity of the proposed solution. Mention alternatives like greedy or branch-and-bound for larger inputs.

5. Test with examples

Walk through a small example to verify the approach, and discuss how to handle edge cases like strings with duplicate characters.

Key Points to Mention

Bitmask representation of character sets for efficient disjointness checks.
Dynamic programming over subsets (if array size is small) or backtracking with pruning.
Time and space complexity: O(2^n) for DP, where n is the number of strings.
The problem is NP-hard (maximum set packing), so exact solutions are exponential.
Handling strings with duplicate characters by discarding them or marking as invalid.
Trade-offs between exact and heuristic approaches for large inputs.

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

How would your approach change if the character set wasn't limited to lowercase English letters, say it included digits, uppercase, or arbitrary Unicode?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

They asked this as a follow-up and I fumbled it a bit.

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AI HintsAI Generated

Suggested Approach

Acknowledge that the core algorithm often remains the same, but the implementation details change: data structures must handle a larger alphabet, and memory/performance trade-offs shift. Discuss specific adaptations like using hash maps instead of fixed arrays, and consider Unicode complexities such as variable-length encoding and normalization.

Pro tip: Demonstrate awareness that Unicode is not just 'more characters'—it introduces grapheme clusters, normalization, and encoding issues that can break naive assumptions. Mentioning these shows depth beyond typical algorithm-focused answers.

1. Identify assumptions tied to the limited character set

Point out where the original solution relied on a small, fixed alphabet (e.g., arrays of size 26, direct indexing by char code).

2. Adapt data structures for a larger alphabet

Replace fixed-size arrays with hash maps or dynamically sized structures, and discuss the impact on time/space complexity.

3. Address Unicode-specific challenges

Consider variable-length encoding (UTF-8), normalization forms, and grapheme clusters; explain how these affect character comparison and counting.

4. Evaluate trade-offs and propose solutions

Discuss performance implications (e.g., hashing overhead, memory usage) and suggest optimizations like using code points or specialized libraries.

5. Summarize the revised approach

Concisely restate how the algorithm changes and confirm that the core logic remains valid with the new data structures.

Key Points to Mention

Hash maps vs. fixed arrays: trade-offs in time and space
Unicode encoding (UTF-8, UTF-16) and variable-length characters
Unicode normalization and grapheme clusters
Impact on algorithm complexity (e.g., O(1) array access becomes O(1) average hash map access)
Potential need for locale-aware or case-insensitive comparisons
Memory considerations for large alphabets

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

How would you convert your recursive subset search into an iterative DP over subsets, and what are the memory tradeoffs?

Algorithms & Data StructuresSystem Design

Author's notes

Didn't get deep into this one, we were running low on time.

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AI HintsAI Generated

Suggested Approach

Start by defining the recursive subset search and identifying overlapping subproblems, then show how to refactor it into an iterative DP over subsets using bitmasks or a bottom-up table. Discuss memory tradeoffs between storing all subsets versus using rolling arrays or in-place updates, and analyze time/space complexity.

Pro tip: Emphasize that the iterative DP often reduces recursion overhead and enables memory optimization, but be ready to discuss when recursion is still preferable (e.g., for sparse state spaces).

1. Clarify the recursive solution

Briefly describe the recursive subset search, including base cases and recurrence relation, to establish a clear starting point.

2. Identify overlapping subproblems and state

Explain how the recursion revisits the same subsets, and define the DP state (e.g., dp[mask] representing the optimal value for a subset).

3. Convert to iterative DP

Show how to iterate over all masks in increasing order, using the recurrence to fill the DP table bottom-up, often leveraging bit manipulation.

4. Analyze memory tradeoffs

Compare memory usage: full DP table (O(2^n)) vs. rolling arrays or in-place updates (O(2^n) but with lower constant), and discuss when to use each.

5. Summarize complexity and tradeoffs

Conclude with time complexity (typically O(n * 2^n)) and space complexity, and mention practical considerations like cache efficiency and recursion depth.

Key Points to Mention

Bitmask representation of subsets
Bottom-up DP iteration order (e.g., increasing mask value)
Space optimization techniques (rolling array, in-place updates)
Time complexity: O(n * 2^n) for both recursive and iterative, but iterative avoids recursion overhead
Memory tradeoffs: full table vs. optimized storage, and impact on cache performance
When to prefer recursion (e.g., sparse states, easier to implement)

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