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Bridge·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Did a technical screen for a Software Engineer role at Bridge with two coding problems back to back. Nothing too wild but the second one had some depth to it if you actually thought about the lazy vs eager tradeoffs.

Questions Asked (2)

Q1

Given a string representing an absolute Unix file path, implement a function to return its canonical (normalized) form. Handle '.', '..', consecutive slashes, and trailing slashes correctly. Walk through your algorithm and give time and space complexity.

Algorithms & Data Structures
Author's notes

Stack-based solution, pretty standard.

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AI HintsAI Generated

Suggested Approach

Use a stack to process each component of the path, handling '.' and '..' appropriately. Split the path by '/', iterate through components, and build the canonical path by pushing valid directory names and popping for '..'. Finally, join the stack with '/' and prepend a leading slash.

Pro tip: Clarify edge cases upfront, like paths with only slashes or multiple '..' at the root, and mention that you'll handle them without errors. Also, discuss how your solution avoids unnecessary string concatenations for efficiency.

1. Clarify requirements and edge cases

Confirm the definition of canonical path: absolute, no '.' or '..', no consecutive slashes, no trailing slash (except root). Ask about edge cases like empty string, root path, and paths with multiple '..' that go above root.

2. Choose data structure and algorithm

Explain that a stack is ideal for processing path components because it naturally handles '..' by popping the last directory. Outline the algorithm: split by '/', iterate, skip empty and '.', pop for '..', push otherwise.

3. Walk through the algorithm with examples

Trace through examples like '/home//foo/' and '/../' to demonstrate handling of consecutive slashes, trailing slashes, and '..' at root. Show how the stack evolves and the final result is constructed.

4. Analyze time and space complexity

State that time complexity is O(n) where n is the length of the path, as each character is processed once. Space complexity is O(n) for the stack and the output string in the worst case.

5. Discuss optimizations and edge cases

Mention that you can avoid splitting into an array by scanning the string and building components on the fly, reducing space. Also, note that the root path '/' is a special case where the stack is empty.

Key Points to Mention

  • Use a stack to handle '..' by popping the last directory.
  • Skip empty strings from consecutive slashes and '.' components.
  • Ensure the final path starts with '/' and has no trailing slash (except for root).
  • Handle '..' at root by ignoring it (cannot go above root).
  • Time complexity O(n) and space complexity O(n).
  • Edge cases: empty string, root path, paths with only slashes, and multiple '..'.

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

Q2

Design an iterator for a nested list structure that can contain integers or other nested lists at arbitrary depth. Implement hasNext() and next() using a lazy stack-based approach, and optionally discuss an eager flattening alternative. Compare the tradeoffs between both designs.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This one actually made me think.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the nested list structure and the iterator interface, then propose a lazy stack-based solution that maintains a stack of iterators or lists to handle arbitrary depth. Walk through the algorithm for hasNext() and next(), and optionally discuss an eager flattening approach using recursion or a stack. Finally, compare the tradeoffs between lazy and eager designs in terms of time/space complexity, memory usage, and suitability for different scenarios.

Pro tip: Emphasize that the lazy approach is more memory-efficient for large or infinite nested structures and avoids unnecessary traversal, but the eager approach can be simpler and faster if the entire structure is small and needs to be traversed multiple times.

1. Clarify requirements and constraints

Ask about the nested list structure (e.g., can it be modified during iteration? what are the expected sizes?) and the iterator interface (hasNext, next, remove?).

2. Design lazy stack-based iterator

Use a stack to keep track of iterators or lists at each level. In hasNext(), peek and advance until an integer is found or stack is empty. In next(), return the next integer.

3. Implement and walk through example

Write pseudocode or actual code for the lazy iterator, and trace through a sample nested list to demonstrate correctness.

4. Discuss eager flattening alternative

Describe how to pre-flatten the nested list into a single list of integers using recursion or an explicit stack, and then iterate over that list.

5. Compare tradeoffs

Analyze time and space complexity, memory usage, and use cases for both approaches. Mention that lazy is better for large/infinite structures, while eager is simpler and faster for small, static structures.

Key Points to Mention

  • Lazy evaluation avoids traversing the entire structure upfront, saving memory and time for large nested lists.
  • Stack-based approach naturally handles arbitrary depth without recursion, avoiding stack overflow risks.
  • Eager flattening simplifies iteration logic but requires O(N) extra space and time upfront.
  • Time complexity: lazy has O(1) amortized per next() and hasNext(), eager has O(N) preprocessing and O(1) per next().
  • Space complexity: lazy uses O(D) where D is depth, eager uses O(N) where N is total number of integers.
  • Consider edge cases: empty nested lists, null values, and modification during iteration.

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