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Openai·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Coding round at OpenAI for a software engineer role, pretty much one problem the whole time with a follow-up that kept expanding. The core question was path simplification but it grew into a full URL processing design discussion before I really saw it coming.

Questions Asked (3)

Q1

Given a Unix-style absolute path string, write a function to simplify it to its canonical form.

Algorithms & Data Structures
Author's notes

Stack-based approach, pretty standard if you've seen it before.

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AI HintsAI Generated

Suggested Approach

Use a stack to process each component of the path, handling '.', '..', and empty segments appropriately. Then reconstruct the canonical path from the stack, ensuring it starts with a single slash and has no trailing slash (except for root).

Pro tip: Clarify edge cases upfront (e.g., multiple slashes, trailing slash, root path) and discuss time/space complexity. Mention that the solution should run in O(n) time and O(n) space, and that using a stack is optimal.

1. Understand the problem and edge cases

Restate the problem in your own words and list edge cases: empty path, root '/', multiple consecutive slashes, '.' and '..' components, trailing slash, and paths that go above root.

2. Choose data structures and algorithm

Decide to split the path by '/' and use a stack (or list) to keep track of valid directory names. Explain why a stack is suitable for handling '..' (pop) and '.' (ignore).

3. Process each component

Iterate over the split components: skip empty strings and '.', pop from stack for '..' if stack is not empty, otherwise push the directory name onto the stack.

4. Reconstruct the canonical path

Join the stack elements with '/' and prepend a '/' to form the absolute path. If the stack is empty, return '/'.

5. Analyze complexity and test

State that time complexity is O(n) where n is the length of the path, and space complexity is O(n) for the stack. Walk through a few test cases to verify correctness.

Key Points to Mention

  • Use of a stack to handle '..' by popping the last directory
  • Ignoring '.' and empty segments (from multiple slashes)
  • Handling edge cases: root path, trailing slash, and paths that go above root
  • Time and space complexity: O(n) time, O(n) space
  • Reconstruction of the path with a single leading slash and no trailing slash
  • Clarifying assumptions with the interviewer before coding

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

Q2

Given a stream of full URLs, how would you count how many times each normalized path appears, and how do you handle case sensitivity, trailing slashes, and percent-encoded segments?

System DesignTechnical Trade-offs
Author's notes

This is where it got interesting and also where I started to sweat a little.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what defines a 'normalized path' and how to handle edge cases. Then outline a streaming algorithm using a hash map to count occurrences, applying normalization rules consistently. Discuss trade-offs of different normalization choices and how they affect correctness and performance.

Pro tip: Mention that normalization should be idempotent and that you'd use a well-tested library for percent-decoding to avoid security issues like path traversal. Also, consider using a trie or count-min sketch for memory efficiency if the cardinality is high.

1. Clarify requirements and edge cases

Ask whether normalization should be case-insensitive, how to treat trailing slashes, and whether to decode percent-encoded segments. Confirm if the path includes query parameters or fragments.

2. Define normalization rules

Specify rules: e.g., lowercase the path, remove trailing slash (except root), decode percent-encoded characters, and resolve dot segments. Ensure rules are applied consistently.

3. Design streaming algorithm

Use a hash map to count occurrences: for each URL, extract the path, normalize it, and increment its count. Discuss memory considerations and potential need for approximate counting.

4. Address trade-offs and scalability

Discuss trade-offs: case sensitivity may be server-dependent; trailing slash removal can merge distinct resources; decoding may introduce ambiguity. For large scale, consider distributed counting or sketches.

5. Summarize and conclude

Recap the approach, emphasizing clarity on normalization and efficiency. Mention testing with edge cases like encoded slashes (%2F) and empty paths.

Key Points to Mention

  • Use a hash map (dictionary) for counting, with normalized path as key.
  • Normalization steps: lowercase, trim trailing slash, percent-decode, resolve dot segments.
  • Case sensitivity: paths are typically case-sensitive, but some servers treat them case-insensitively; clarify.
  • Trailing slashes: often equivalent to no trailing slash, but not always (e.g., /foo vs /foo/).
  • Percent-encoding: decode to compare equivalent paths, but beware of encoded slashes (%2F) which may be significant.
  • Scalability: consider memory usage; use approximate counting (e.g., count-min sketch) if cardinality is huge.

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

Q3

How would your design extend to support relative paths or symlinks, and what are the complexity implications?

System DesignAlgorithms & Data Structures
Author's notes

Caught me a bit flat-footed.

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AI HintsAI Generated

Suggested Approach

First clarify the current design's assumptions about paths (e.g., absolute, canonical) and then explain how to extend it to handle relative paths and symlinks by introducing resolution logic. Discuss the complexity implications in terms of time (e.g., path resolution, cycle detection) and space (e.g., caching, recursion depth).

Pro tip: Mention real-world examples like how Unix resolves symlinks with a maximum depth to prevent infinite loops, and how caching resolved paths can mitigate repeated resolution costs.

1. Clarify assumptions and requirements

State the current design's handling of paths (e.g., assumes absolute, canonical paths) and define what relative paths and symlinks mean in this context.

2. Design resolution algorithm

Outline an algorithm to resolve relative paths (e.g., using current working directory) and symlinks (e.g., following links with cycle detection).

3. Analyze complexity

Break down time and space complexity: resolution time proportional to path depth and symlink chain length; space for caching or recursion stack.

4. Discuss optimizations and trade-offs

Propose caching resolved paths, limiting symlink depth, or using iterative resolution to avoid stack overflow, and discuss trade-offs.

5. Conclude with impact

Summarize how these extensions affect overall system performance, scalability, and correctness.

Key Points to Mention

  • Path resolution algorithm (e.g., realpath, canonicalization)
  • Cycle detection for symlinks (e.g., visited set, max depth)
  • Time complexity: O(n) where n is path components or symlink chain length
  • Space complexity: O(n) for recursion or O(1) with iterative approach
  • Caching resolved paths to improve performance
  • Security considerations (e.g., symlink attacks, path traversal)

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