Coding Algorithms And Data Structures

What's being tested

Amazon MLE coding screens test whether you can turn ML-adjacent production tasks into clean algorithms: batching, clustering, training loops, graph reachability, interval allocation, and top-k retrieval. Interviewers look for correct data structures, complexity analysis, edge-case handling, and code that would survive inside a training or serving pipeline.

Patterns & templates

• K-means implementation — initialize centroids, assign by nearest distance, recompute means; stop on max iterations or centroid shift $\lt \epsilon$.

• Interval merging / allocation — sort by start time, scan once, merge or consume ranges; usually O(n log n) time from sorting.

• Top-k frequency retrieval — use collections.Counter plus heapq.nlargest for O(n log k), or bucket counts for bounded frequencies.

• Directed cycle check — adding edge u -> v creates a cycle iff u is reachable from v; solve with DFS/BFS.

• PyTorch training loop — order matters: model.train(), move tensors to device, optimizer.zero_grad(), forward, loss, backward(), step().

• Bucket batching optimization — sort or group examples by sequence length/cost, then pack batches to reduce padding and GPU underutilization.

• Event-driven queues — model backorders or pending work with deque, heapq, or ordered maps; define FIFO vs priority semantics explicitly.

Common pitfalls

Pitfall: Writing ML pseudocode without executable edge handling, such as empty clusters in K-means or zero-length batches in bucketing.

Pitfall: Missing complexity tradeoffs; Amazon interviewers expect O(V+E), O(n log n), memory cost, and when the approach breaks at scale.

Pitfall: In PyTorch loops, forgetting optimizer.zero_grad() or device movement silently produces wrong training behavior or runtime errors.

Practice these

The practice cards below cover the canonical variants — solve all of them and time yourself.