Algorithms, Data Structures, And Complexity Analysis
Asked of: ML Engineer
Last updated
What's being tested
These questions probe core asymptotic analysis, algorithmic pattern recognition, and practical memory/time accounting for ML workloads: matrix ops, grid propagation, in-memory key-value semantics, vectorized numeric code, and search under API constraints. Interviewers expect clear framing (assumptions + invariants), selection of the right algorithmic template, and honest tradeoff reasoning for production ML pipelines.
Patterns & templates
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Multi-source BFS / wavefront simulation — use queue with visited counts; runs in O(V+E) for grid propagation, handle boundary/threshold updates.
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Naive vs blocked matrix multiply — naive O(n^3); use cache blocking (tile GEMM) and BLAS to approach hardware peak.
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Hash table for KV ops — average O(1) insert/delete; mention load factor, resizing costs, and tombstones for logical deletes.
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NumPy vectorization & broadcasting — prefer
`np`vector ops, use`np.broadcast_to`semantics; avoid Python loops to keep O(n) per-element. -
Binary / exponential search under monotonicity — O(log n) if predicate monotonic; if non-monotonic, fall back to linear scan or constrained probing.
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Complexity accounting for ML arrays — size = elements * bytes_per_element (float32 = 4B); factor in temporary arrays for intermediate ops.
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Rate-limited black-box API patterns — memoize calls, batch tests, exponential backoff, and minimize queries by narrowing search space.
Common pitfalls
Pitfall: Confusing algorithmic steps with constant-factor optimizations — saying "use BLAS" isn't a complexity proof; justify asymptotic gains.
Pitfall: Ignoring memory for temporaries — vectorized code can OOM if you create many copies; count peak memory, not just input size.
Pitfall: Assuming monotonicity for binary search — always confirm predicate behavior; binary search on non-monotonic APIs yields wrong answers.
Practice these
The practice cards below cover the canonical variants — solve all of them and time yourself.
Practice questions
- Analyze matrix multiplication complexityOpenAI · Machine Learning Engineer · Technical Screen · hard
- Compute time to infect all cellsOpenAI · Machine Learning Engineer · Onsite · hard
- Find earliest supporting version under constraintsOpenAI · Machine Learning Engineer · Technical Screen · Medium
- Implement vectorized NumPy ops and explain broadcastingOpenAI · Machine Learning Engineer · Onsite · Medium
- Find earliest supporting dependency versionOpenAI · Machine Learning Engineer · Technical Screen · Medium
- Implement in-memory database insert and delete operationsOpenAI · Machine Learning Engineer · Onsite · hard
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