Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Meta MLE interview with a simulation-based coding problem. Nothing crazy on the surface but the implementation details got tricky fast.

Questions Asked (1)

Given a card draw strategy, simulate the card game across many random shuffles and compute the percentage of runs where the strategy achieves a perfect score (all possible sets found).

Algorithms & Data StructuresA/B Testing & Experimentation

Author's notes

Took me a minute to even parse what they were asking.

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AI HintsAI Generated

Suggested Approach

Clarify the game rules and strategy, then design a simulation that runs many random shuffles, tracks perfect scores, and computes the percentage. Discuss implementation details, statistical considerations, and potential optimizations.

Pro tip: Mention that you would first validate the simulation with a small number of runs and compare against analytical results if possible, to ensure correctness before scaling up. Also, discuss how you would handle randomness and reproducibility by setting seeds.

1. Clarify the problem

Ask questions to fully understand the card game rules, the specific strategy, and what constitutes a 'perfect score'. Confirm the number of runs needed and any constraints.

2. Design the simulation

Outline the simulation loop: for each run, shuffle the deck, execute the strategy, and check if all sets are found. Use efficient data structures to track state.

3. Implement and test

Write clean, modular code for shuffling, strategy execution, and scoring. Test with a small number of runs and verify against edge cases or analytical solutions.

4. Run large-scale simulation

Execute the simulation for a large number of runs (e.g., 10,000 or more) to estimate the percentage. Consider parallelization or vectorization for speed.

5. Analyze and report results

Compute the percentage of perfect runs, along with confidence intervals. Discuss statistical significance and potential sources of error.

Key Points to Mention

Monte Carlo simulation methodology and its appropriateness for estimating probabilities.
Efficient shuffling algorithms (e.g., Fisher-Yates) and avoiding bias.
Data structures for tracking found sets and game state (e.g., hash sets, bitmasks).
Statistical measures: confidence intervals, standard error, and number of runs needed for desired precision.
Reproducibility: setting random seeds and documenting the simulation setup.
Performance optimization: parallelization, vectorization, or using compiled languages for speed.

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