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Perplexity·Software Engineer·Online Assessment (OA)·Senior

SeniorPrefer not to say
Apr 2026Remote

Summary

Perplexity gave me a timed coding assessment centered on tokenization, basically asking me to reimplement a reference algorithm but fast. The problem was well-scoped but the performance bar was real, and I spent way too long second-guessing whether my output was even correct before worrying about speed.

Questions Asked (1)

Q1

Given a reference tokenization algorithm in a slow method, implement a faster version that produces identical output while tokenizing roughly 500KB of text against a 10,000+ token alphabet within a 10-second time budget.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The 'identical output' constraint tripped me up more than the performance part.

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AI HintsAI Generated

Suggested Approach

Start by thoroughly understanding the reference algorithm's exact behavior, including edge cases like overlapping matches and tie-breaking. Then design a faster implementation using efficient data structures (e.g., trie or hash map) and algorithms (e.g., greedy longest-match with precomputed lookups), ensuring identical output. Validate with extensive tests and benchmark against the 10-second budget.

Pro tip: Focus on algorithmic complexity first—a trie-based longest-match tokenizer can reduce time from O(n*m) to O(n*L) where L is max token length. Also, mention that you'd profile the reference to identify bottlenecks before optimizing.

1. Understand the Reference Algorithm

Read and trace the slow tokenization method to capture its exact semantics: tokenization rules, matching strategy (e.g., longest match, first match), handling of overlaps, and any special cases. Write down invariants that must hold.

2. Design a Faster Data Structure

Choose an efficient structure like a trie (prefix tree) or a hash map of token prefixes to enable quick lookups. Consider memory vs. speed trade-offs and precompute any necessary metadata (e.g., max token length).

3. Implement the Optimized Algorithm

Implement tokenization using the chosen structure, ensuring it replicates the reference's behavior exactly. Use techniques like greedy longest-match with backtracking only when needed, and avoid unnecessary string operations.

4. Validate Correctness

Create a test harness that compares outputs of the reference and optimized versions on diverse inputs, including edge cases (empty text, unknown characters, overlapping tokens). Use property-based testing if possible.

5. Benchmark and Optimize

Measure performance on 500KB of text with a 10,000+ token alphabet. If needed, optimize further (e.g., caching, parallelization, or memory layout) while ensuring correctness. Confirm it runs well within 10 seconds.

Key Points to Mention

  • Algorithmic complexity analysis: comparing O(n*m) vs O(n*L) and why it matters for large inputs.
  • Use of a trie or hash map for efficient token lookup, and how to handle variable token lengths.
  • Correctness verification: ensuring identical output to the reference, including edge cases and tie-breaking rules.
  • Performance benchmarking: measuring time and memory, and optimizing hot paths (e.g., avoiding string concatenation).
  • Trade-offs between precomputation time/memory and runtime speed, especially with a large alphabet.
  • Handling of Unicode or multi-byte characters if applicable, and ensuring the solution scales.

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