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Plaid·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Interviewed for an MLE role at Plaid and got a data structure question that looked deceptively simple on the surface. Two mappings, one lookup function, and a bunch of edge cases to reason through.

Questions Asked (1)

Q1

You have two mappings: one from bank codes to canonical institution IDs, and another from institution IDs to one or more bank records. Preprocess these mappings and implement a lookup function that, given a bank code, returns all associated banks. Walk through your data structure choices, time complexities, and how you handle missing codes or duplicate entries.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

My first instinct was to just chain the two lookups at query time and call it done.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the input mappings and expected output, then propose a two-step preprocessing that builds a direct bank_code -> list of bank records dictionary. Discuss time/space complexity and edge cases like missing codes and duplicates, and mention how this scales for ML feature engineering or real-time lookup.

Pro tip: Emphasize that preprocessing is a one-time cost that enables O(1) lookups, which is critical for latency-sensitive ML serving. Also, mention that you would validate and log anomalies (e.g., duplicate bank codes mapping to different institutions) to maintain data quality.

1. Clarify requirements and assumptions

Ask about the size of mappings, whether they fit in memory, and if updates are frequent. Confirm that the output should be all banks associated with a given bank code, and discuss handling of missing codes (return empty list or raise error).

2. Design preprocessing pipeline

Iterate through the bank_code -> institution_id mapping, and for each, look up the institution_id in the institution_id -> banks mapping. Build a new dictionary mapping bank_code directly to a list of bank records, merging duplicates if necessary.

3. Analyze time and space complexity

Preprocessing takes O(N + M) time where N is number of bank codes and M is total bank records, and O(K) space where K is total output entries. Lookup is O(1) average time. Discuss trade-offs vs. on-the-fly lookup.

4. Handle edge cases and data quality

Address missing bank codes (return empty list), duplicate bank codes (merge lists, deduplicate if needed), and inconsistent mappings (log warnings). Consider if bank records can be duplicated across institutions.

5. Discuss scalability and ML integration

Mention how this structure can be serialized for batch scoring or embedded in a feature store. For real-time serving, consider caching or using a key-value store if the mapping is large.

Key Points to Mention

  • Use a hash map (dictionary) for O(1) average lookup time.
  • Preprocessing flattens the two-level mapping into a single-level mapping for efficiency.
  • Handle missing codes gracefully by returning an empty list or a default value.
  • Deduplicate bank records if the same bank appears multiple times for a code.
  • Consider memory footprint and whether to store bank IDs or full records.
  • Mention potential for using this in feature engineering pipelines for ML models.

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