Anthropic·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Anthropic ML engineer interview with a live coding problem on a Colab notebook. The question was about building a weighted data batching class, which sounds straightforward until you're actually staring at it and trying to think about edge cases in real time.

Questions Asked (1)

Given a mapping of dataset names to weights, implement a DataBatcher class that produces batches of a fixed size by drawing items from each dataset proportionally to its weight. You can assume batch_size is divisible by the total weight sum. Discuss correctness and complexity.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The setup was actually pretty readable, they gave you the DataRegistry class already written so you weren't starting from scratch.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then design a deterministic algorithm that allocates batch slots proportionally to weights. Implement the DataBatcher class with a method to generate batches, ensuring correctness and analyzing time/space complexity. Discuss trade-offs and potential edge cases.

Pro tip: Mention that you can precompute the per-batch allocation once to avoid repeated calculations, and use a round-robin or interleaving strategy to avoid ordering bias. Also, highlight that the solution should handle dynamic changes to weights if required.

1. Clarify Requirements

Confirm assumptions: batch_size divisible by total weight sum, weights are integers, and whether weights can change. Ask about expected batch size and dataset sizes.

2. Design Allocation Strategy

Compute the number of items per dataset per batch as (weight / total_weight) * batch_size. Since batch_size is divisible by total_weight, this is an integer. Decide on ordering within the batch (e.g., interleave or group by dataset).

3. Implement DataBatcher Class

Create a class that stores dataset names, weights, batch size, and precomputed counts. Implement a method to generate a batch by sampling the required number of items from each dataset, possibly using iterators or indices.

4. Analyze Correctness and Complexity

Prove that each batch has exactly batch_size items and that the proportion of items from each dataset matches the weights. Analyze time complexity: O(batch_size) per batch to assemble, and space O(num_datasets) for counts.

5. Discuss Trade-offs and Extensions

Consider alternative approaches like weighted random sampling (which may not guarantee exact proportions per batch) and discuss when deterministic allocation is preferable. Mention handling of non-divisible cases or dynamic weights.

Key Points to Mention

Proportional allocation: each dataset contributes (weight / total_weight) * batch_size items per batch.
Deterministic vs. stochastic sampling: deterministic ensures exact proportions per batch, while stochastic may vary.
Ordering within batch: interleaving datasets can prevent bias in training (e.g., avoid all items from one dataset consecutively).
Time complexity: O(batch_size) per batch to assemble, O(1) to compute counts if precomputed.
Space complexity: O(num_datasets) to store counts and iterators.
Edge cases: handling datasets with zero weight, empty datasets, and ensuring batch_size divisible by total weight sum.

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