Upstart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Coding screen for a software engineer role at Upstart. One meaty string manipulation problem that looks straightforward until you actually sit down to implement it cleanly.

Questions Asked (1)

Implement a function that takes a text string and computes each word's 'letter span', defined as the absolute difference between the alphabetic positions of the first and last alphabetic characters in the word (a=0, z=25, case-insensitive, ignoring punctuation). Return both the maximum span found and all words from the text that achieve it. Discuss how you handle ties, duplicates, contractions, hyphenated words, and inputs with no alphabetic content, and analyze time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I felt pretty good about the core logic until the edge cases started piling up.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem's edge cases and assumptions with the interviewer, then outline a linear-time algorithm that processes each word, computes its span, and tracks the maximum and associated words. Finally, discuss trade-offs in handling ties, duplicates, and special word forms, and analyze complexity.

Pro tip: Explicitly state your assumptions about what constitutes a 'word' (e.g., splitting on whitespace) and how you handle contractions and hyphenated words, as this demonstrates attention to detail and prevents misunderstandings. Also, mention that you would write unit tests for edge cases like empty input and words with no alphabetic characters.

1. Clarify requirements and edge cases

Ask the interviewer to confirm definitions: what separates words (whitespace?), how to treat contractions (e.g., 'don't' as one word or two?), hyphenated words (e.g., 'well-known' as one word?), and whether to include duplicates in the output. Also clarify behavior for inputs with no alphabetic content.

2. Design the algorithm

Propose a single-pass approach: split the text into words, for each word extract the first and last alphabetic characters (ignoring punctuation), compute the absolute difference of their positions (a=0, z=25), and track the maximum span and all words achieving it. Use a list to collect words for the current maximum, resetting when a new maximum is found.

3. Handle ties, duplicates, and special cases

Explain that ties are handled by collecting all words with the maximum span; duplicates are included as separate entries if they appear multiple times. For contractions and hyphenated words, state your chosen interpretation (e.g., treat as single words) and adjust the splitting logic accordingly. If no alphabetic characters exist in any word, return a maximum span of 0 and an empty list (or as specified).

4. Analyze complexity and optimize

State that the algorithm runs in O(n) time where n is the total number of characters, as each character is processed once. Space complexity is O(m) where m is the number of words achieving the maximum span (or O(1) extra space if only tracking the maximum and a list of words). Mention that this is optimal for a single-pass solution.

5. Summarize and invite feedback

Concisely recap the approach, emphasizing how you addressed the edge cases and trade-offs. Invite the interviewer to ask about alternative interpretations or optimizations, showing openness to collaboration.

Key Points to Mention

Definition of 'word' and splitting strategy (e.g., whitespace splitting, handling punctuation).
Case-insensitive mapping of letters to positions (a=0, z=25).
Handling of ties: collect all words with the maximum span.
Handling of duplicates: include each occurrence separately.
Treatment of contractions and hyphenated words: decide whether to split or keep as single tokens.
Edge case: input with no alphabetic characters (return max span 0 and empty list).
Time complexity O(n) and space complexity O(m) for output list.
Potential optimizations: early termination if maximum possible span (25) is found.

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