Google·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

Coding round for an ML Engineer role at Google. The question looked like a standard Python exercise but had enough edge cases baked in that it turned into a pretty involved conversation about generator design and test coverage.

Questions Asked (1)

Write a Python generator that yields integers from a list, handling None values, empty input, duplicates, very large inputs, and non-integer items. You need to define and document what the generator does with invalid entries (skip, convert, or raise), then write comprehensive unit tests covering normal behavior, edge cases, iteration order, resource usage, and error handling.

Algorithms & Data StructuresTechnical Trade-offsAPI & Integrations

Author's notes

The generator part itself wasn't the hard bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and explicitly defining the generator's behavior for invalid inputs (skip, convert, or raise). Then implement a clean, documented generator that handles edge cases, and finally write comprehensive unit tests covering normal behavior, edge cases, iteration order, resource usage, and error handling.

Pro tip: In interviews, always state your assumptions about ambiguous requirements (e.g., what counts as an integer) and justify your design choices in terms of trade-offs like robustness vs. strictness. This shows you think like a senior engineer.

1. Clarify requirements and define behavior

Ask clarifying questions about what to do with None, non-integers, and duplicates. Explicitly state your chosen policy (e.g., skip None and non-integers, yield duplicates as-is) and document it in the generator's docstring.

2. Design the generator

Write a generator function that iterates over the input list, applies the defined policy, and yields integers. Ensure it handles empty input gracefully and works with very large inputs by being lazy (not converting the entire list to a new list).

3. Implement and document

Code the generator with clear comments and a docstring explaining the behavior for each edge case. Use type hints to specify input and output types.

4. Write comprehensive unit tests

Create tests for normal behavior (list of ints), edge cases (empty list, all None, all non-ints), duplicates, large inputs (e.g., using a generator to simulate), iteration order, and error handling (if raising is chosen). Use a testing framework like pytest.

5. Discuss trade-offs and resource usage

Explain why you chose skip/convert/raise, and how the generator is memory-efficient for large inputs. Mention that tests for resource usage can be done by checking that the generator doesn't consume excessive memory (e.g., using tracemalloc or by design).

Key Points to Mention

Explicit policy for invalid entries: skip, convert, or raise, with justification.
Lazy evaluation and memory efficiency for very large inputs.
Handling of duplicates: preserve order and yield duplicates as they appear.
Comprehensive unit tests: normal, edge cases, iteration order, resource usage, error handling.
Use of type hints and docstrings for clarity and maintainability.
Trade-offs between strictness (raising errors) and robustness (skipping/converting).

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