Apple·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Apple ML engineer interview with a coding problem centered on implementing a TF-IDF scorer from scratch. The problem looked like a search relevance exercise but had enough edge cases in the math to keep things interesting.

Questions Asked (1)

Implement a simplified TF-IDF scorer: given a list of documents and a query string, compute a score for each document using term frequency and smoothed inverse document frequency, then return the document indices sorted by descending score, breaking ties by smaller index.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The formula they gave had that +1 smoothing on both numerator and denominator of the IDF, plus an extra +1 added to the whole log.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the TF-IDF variant: term frequency as raw count or normalized, and smoothed IDF as log((N+1)/(df+1)) + 1. Then outline an efficient implementation using a vocabulary mapping and precomputed document frequencies, and finally discuss scoring, sorting, and tie-breaking.

Pro tip: Mention that you would precompute document frequencies and use a hash map for O(1) term lookups, and that you would handle out-of-vocabulary query terms gracefully by ignoring them. This shows production awareness.

1. Clarify requirements and definitions

Confirm the exact TF and IDF formulas, whether to normalize TF, and how to handle unseen terms. Also clarify tie-breaking rules and output format.

2. Preprocess and build vocabulary

Tokenize documents and query (e.g., lowercase, split on whitespace). Build a vocabulary mapping each unique term to an index, and compute document frequency (df) for each term.

3. Compute TF-IDF vectors

For each document, compute term frequency (tf) for each term. Compute IDF using smoothed formula: idf = log((N + 1) / (df + 1)) + 1. Multiply tf and idf to get the weight for each term.

4. Score documents against query

For each document, sum the TF-IDF weights for terms that appear in the query. If a query term is not in the document, its contribution is zero. Optionally normalize by document length.

5. Sort and return results

Sort document indices by descending score, breaking ties by smaller index. Return the sorted list.

Key Points to Mention

Smoothed IDF formula: log((N + 1) / (df + 1)) + 1 to avoid division by zero and negative values.
Term frequency can be raw count or normalized (e.g., tf / max_tf or tf / doc_length); clarify which is expected.
Efficient implementation: precompute df and use dictionaries for O(1) lookups; avoid recomputing IDF per document.
Handling out-of-vocabulary query terms: ignore them or assign zero weight.
Tie-breaking: sort by score descending, then by index ascending; can use a custom comparator or stable sort with index as secondary key.
Complexity: O(D * L + Q * D) where D is number of documents, L average document length, Q number of query terms; sorting adds O(D log D).

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