Anthropic·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Interviewed for an ML Engineer role at Anthropic, and the technical question was genuinely interesting but also kind of brutal if you hadn't thought carefully about stateful data pipelines before. One question, deep rabbit hole.

Questions Asked (1)

You have a DataBatcher that draws weighted samples from multiple datasets per batch. Extend it to support an offset parameter so that after checkpointing a run, a new batcher initialized with that offset produces bit-for-bit identical batches to an uninterrupted run. What state needs to be persisted, and how do you make this robust to dataset or weight changes later?

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

I started okay, talked about storing per-dataset cursor positions and making sure any RNG state gets serialized too.

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AI HintsAI Generated

Suggested Approach

First, clarify the requirements: the offset must allow resuming from a checkpoint such that the sequence of batches is identical to an uninterrupted run, even if datasets or weights change later. Then, design the batcher to maintain a deterministic state (e.g., a global sample index or RNG state) that can be serialized and restored, and ensure that any changes to datasets or weights are versioned or hashed so that the offset remains valid only for the same configuration. Finally, discuss trade-offs between storing minimal state (e.g., just an integer offset) versus storing more context (e.g., dataset versions, weight snapshots) to handle changes robustly.

Pro tip: Emphasize that bit-for-bit reproducibility requires controlling all sources of randomness and iteration order; consider using a deterministic pseudo-random number generator seeded by the offset and dataset identifiers, and persist the RNG state if the sampling algorithm consumes randomness in a non-linear way.

1. Define the offset semantics

Determine what the offset represents: a global sample index, a batch index, or an RNG seed. Clarify whether it should account for dataset order and weighting, and how it interacts with checkpointing.

2. Identify state to persist

List all state needed to reproduce the exact sequence: current position in each dataset, RNG state, accumulated weights, and any dataset-specific metadata (e.g., version hashes). Decide what can be recomputed from the offset and what must be stored explicitly.

3. Design for determinism

Ensure the sampling algorithm is deterministic given the state: use a fixed iteration order, avoid floating-point non-determinism, and seed RNGs consistently. Consider using a counter-based RNG or a hash of (offset, dataset_id) to generate per-sample randomness.

4. Handle dataset and weight changes

Implement versioning or hashing of datasets and weights. When a change is detected, either invalidate the offset (requiring a new checkpoint) or provide a migration path that maps the old offset to the new configuration, possibly with a warning about non-identical batches.

5. Validate and test

Write tests that simulate interruption and resumption, comparing batches bit-for-bit. Test edge cases: dataset addition/removal, weight changes, and offset beyond dataset size. Document assumptions and limitations.

Key Points to Mention

Deterministic sampling: use a seeded RNG or counter-based approach to ensure reproducibility.
State serialization: persist the minimal state (e.g., global step, RNG state, dataset versions) in the checkpoint.
Dataset versioning: hash or version datasets and weights to detect changes that would break reproducibility.
Weight handling: store weights or a hash of weights; if weights change, the offset may no longer be valid.
Error handling: define behavior when offset exceeds dataset size or when datasets are modified (e.g., raise error or fallback).
Performance: consider the overhead of storing and restoring state, and whether the offset can be computed cheaply.

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