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Liftoff·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Liftoff SWE interview with a two-part string encoding problem. The first part was a decode function which was manageable, but the second part asking for an optimal encoder with a pattern-length constraint is where things got interesting and a bit painful.

Questions Asked (2)

Q1

Implement a decoder for a run-length encoding scheme where each token is a count followed by a pattern of lowercase letters, and token boundaries are determined by digit-to-letter transitions.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Parsing was the main thing to get right.

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AI HintsAI Generated

Suggested Approach

Clarify the encoding format and edge cases, then design a single-pass parser that accumulates digits until a letter is encountered, at which point it emits the repeated pattern. Discuss time/space complexity and potential optimizations like streaming or handling large counts.

Pro tip: Mention that you would validate the input and handle malformed tokens gracefully, and consider whether the output should be built as a list of strings for efficiency before joining.

1. Clarify the problem

Ask questions to confirm the exact encoding rules, such as whether counts can be multi-digit, if patterns can be empty, and what characters are allowed. Also clarify expected input size and output format.

2. Outline the algorithm

Describe a linear scan: iterate through the string, building the current count when digits are seen, and when a letter is encountered, append the letter repeated count times to the result. Reset the count after each token.

3. Handle edge cases

Discuss handling of invalid inputs (e.g., missing count, zero count, non-digit/non-letter characters) and how to report errors. Consider empty input and very large counts that could cause memory issues.

4. Analyze complexity and trade-offs

State that time complexity is O(n + m) where n is input length and m is output length, and space is O(m) for the output. Mention alternatives like streaming output or using a generator to avoid building the entire string in memory.

5. Test with examples

Walk through a few test cases, including simple ones like '3a2b' -> 'aaabb', multi-digit counts like '12a', and edge cases like '0a' or 'a' (if invalid).

Key Points to Mention

  • Single-pass parsing with digit accumulation
  • Handling multi-digit counts correctly
  • Edge cases: empty input, zero count, missing count, invalid characters
  • Time and space complexity analysis
  • Potential optimizations: streaming output, using StringBuilder/list for efficiency
  • Input validation and error handling strategy

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

Q2

Given a string and an integer K, produce the shortest possible encoded string using the same RLE format where every pattern has length at most K. Optimize for minimum total character count in the encoded output.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This is the one that got me.

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AI HintsAI Generated

Suggested Approach

Clarify the RLE format and constraints, then propose a dynamic programming solution that considers all possible pattern lengths up to K. Discuss trade-offs between time and space complexity, and test with edge cases.

Pro tip: Mention that the optimal encoding might not be greedy; DP is needed to handle cases where shorter patterns lead to better overall compression. Also, discuss how to handle patterns that repeat non-consecutively.

1. Clarify the Problem

Ask clarifying questions about the RLE format (e.g., how counts are encoded, whether patterns can overlap) and constraints (string length, K value). Confirm that the goal is to minimize total characters in the encoded output.

2. Define the DP State

Define dp[i] as the minimum encoded length for the prefix ending at index i. Consider transitions by trying all pattern lengths L from 1 to K that can be repeated consecutively starting at some position.

3. Optimize Transitions

For each position, precompute the maximum number of times each pattern of length L repeats consecutively. Use this to efficiently compute dp transitions, possibly with a sliding window or precomputed arrays.

4. Handle Encoding Details

Account for the character cost of encoding the count (e.g., number of digits) and the pattern itself. Ensure the encoding format is correctly applied, including any delimiters.

5. Analyze Complexity and Test

Analyze time and space complexity (e.g., O(N*K) time). Walk through examples, including edge cases like K=1, all same characters, and patterns that repeat non-consecutively.

Key Points to Mention

  • Dynamic programming is necessary because greedy approaches can fail; the optimal encoding may require choosing shorter patterns to allow better compression later.
  • The encoding cost includes both the pattern characters and the count representation (e.g., digits), which affects the total length.
  • Patterns can be of any length up to K, and they must appear consecutively to be encoded together.
  • Time complexity can be optimized by precomputing repetition counts for each pattern length, avoiding redundant checks.
  • Edge cases: K=1 (no compression possible), strings with no repeats, and patterns that repeat but are separated by other characters.
  • Trade-offs: larger K allows longer patterns but increases DP state space; consider if K is large, maybe use a different approach.

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