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Openai·Machine Learning Engineer·Onsite - Coding / Algorithms·Senior

Senior
Jun 2026

Summary

Coding round for an ML Engineer role at OpenAI. The whole thing was a two-stage design problem around building a resumable iterator, and you were expected to write tests before writing any implementation. Felt more like a software engineering interview than anything ML-specific.

Questions Asked (3)

Q1

Design a resumable iterator over an in-memory list. It should support checking for remaining elements, returning the next element, saving its current position as a portable checkpoint, and reconstructing from that checkpoint so iteration picks up exactly where it left off.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

The checkpoint-not-being-a-live-reference thing tripped me up at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what does 'portable checkpoint' mean (e.g., serializable, versioned, independent of iterator instance)? Then design a class that maintains an index into the list, with methods to check remaining elements, get next, and produce/consume a checkpoint. Discuss trade-offs like immutability, thread-safety, and handling list mutations.

Pro tip: Emphasize that the checkpoint should be a simple, serializable value (like an integer index) and that the iterator should be stateless beyond that index, making it easy to reconstruct. Also mention that if the underlying list can change, you need to define semantics (e.g., snapshot or versioning) to ensure correctness.

1. Clarify requirements and constraints

Ask about the nature of the list (immutable? mutable?), what 'portable' means (e.g., JSON-serializable, cross-process), and whether concurrency is a concern. This ensures you design the right abstraction.

2. Define the iterator interface

Outline methods: hasNext(), next(), saveCheckpoint(), and a static or factory method to create an iterator from a checkpoint. Specify return types and error handling (e.g., NoSuchElementException).

3. Design the checkpoint representation

Choose a simple, serializable format (e.g., an integer index or a small object with index and maybe a version/ID). Explain why this is portable and how it enables reconstruction.

4. Implement the iterator logic

Describe how the iterator maintains its current position, how next() advances it, and how saveCheckpoint() captures the current index. Show how reconstruction sets the index from the checkpoint.

5. Discuss trade-offs and edge cases

Address mutability of the underlying list (e.g., if elements are added/removed, checkpoints may become invalid), thread-safety, and performance (O(1) operations). Mention possible solutions like versioning or copying.

Key Points to Mention

  • Checkpoint should be a simple, serializable value (e.g., integer index) to ensure portability.
  • Iterator should be stateless except for the current index, making it easy to reconstruct.
  • Define behavior when the underlying list is modified after checkpoint creation (e.g., throw exception, use versioning, or document undefined behavior).
  • Consider thread-safety if multiple threads might use the iterator or checkpoints concurrently.
  • Ensure O(1) time complexity for hasNext(), next(), and saveCheckpoint().
  • Provide a clear API for creating an iterator from a checkpoint, possibly as a static factory method.

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

Q2

Now extend the same save/resume contract to a file-based iterator that streams lines one at a time. The checkpoint must work across process restarts and must not load the whole file into memory.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

This is where it got interesting.

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AI HintsAI Generated

Suggested Approach

Design a lazy line iterator that reads the file in chunks and yields lines, maintaining a checkpoint of byte offset and line number. On resume, seek to the saved offset and skip any partial line, then continue yielding from the next line. Ensure the checkpoint is persisted atomically (e.g., to a separate file) after each line or batch to survive process restarts.

Pro tip: Emphasize that the checkpoint must be updated atomically and that the iterator should be idempotent on resume—re-reading the last line is acceptable if you track line numbers, but skipping or duplicating lines is not. Also, mention that you'd use a buffered reader to avoid excessive syscalls while keeping memory bounded.

1. Define the save/resume contract

Specify what state constitutes a checkpoint: byte offset, line number, and possibly a hash of the last line for validation. Clarify that the contract must work across process restarts and not load the whole file.

2. Design the lazy iterator

Implement a generator that reads the file in fixed-size chunks (e.g., 64KB) and yields lines one at a time. Use a buffer to handle lines that span chunk boundaries without loading the entire file.

3. Implement checkpointing

After yielding each line (or every N lines), atomically persist the current byte offset and line number to a checkpoint store (e.g., a JSON file). Use write-to-temp-then-rename for atomicity.

4. Implement resume logic

On startup, load the checkpoint, seek to the saved byte offset, and skip any partial line (e.g., read until newline). Then continue yielding lines from the next line, ensuring no duplication or loss.

5. Handle edge cases and trade-offs

Discuss handling of partial lines, file modifications, checkpoint frequency vs. performance, and memory bounds. Mention that checkpointing every line may be slow, so batching is a trade-off.

Key Points to Mention

  • Lazy evaluation and streaming to avoid loading the whole file into memory
  • Checkpoint state: byte offset, line number, and optional validation hash
  • Atomic checkpoint persistence (write-to-temp + rename) to survive crashes
  • Resume logic: seek to offset, skip partial line, continue from next line
  • Trade-offs: checkpoint frequency vs. performance, memory vs. I/O
  • Idempotency and exactly-once semantics on resume

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

Q3

Write the test suite before implementing anything. Tests must cover resuming from the start, from the middle, from the end, a full round-trip checkpoint-and-reconstruct, and a case asserting that calling save_state() does not mutate the original iterator.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Test-first was the explicit constraint and they watched closely to see if I'd skip it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the iterator's interface and checkpoint format, then outline a test suite that covers all specified scenarios using a table-driven or parameterized approach. Write tests first to drive the implementation, ensuring each test is isolated and uses a mock or simple iterator to verify behavior without side effects.

Pro tip: Use property-based testing to assert that save_state() is pure and that resuming from any checkpoint yields the same sequence as the original iterator, catching subtle mutation bugs early.

1. Clarify requirements and interface

Ask questions to confirm the iterator's API, checkpoint format, and expected behavior for edge cases like empty iterators or invalid checkpoints.

2. Design test cases

List the required scenarios: resume from start, middle, end, full round-trip, and no mutation on save_state. Consider additional edge cases like multiple checkpoints or concurrent saves.

3. Implement tests with isolation

Write each test using a fresh iterator instance and assert expected outcomes. Use mocking or a simple list-based iterator to avoid dependencies.

4. Run tests and iterate

Execute the test suite to see failures, then implement the iterator to pass tests. Refactor tests for clarity and coverage.

5. Review and extend

Ensure tests cover all specified cases and consider adding property-based tests for robustness. Discuss trade-offs like test speed vs. thoroughness.

Key Points to Mention

  • Test-driven development (TDD) approach: write tests before implementation.
  • Parameterized or table-driven tests to cover multiple resume points efficiently.
  • Immutability of save_state(): assert original iterator state is unchanged.
  • Round-trip test: save state, reconstruct iterator, verify same sequence.
  • Edge cases: empty iterator, checkpoint at boundaries, invalid checkpoint handling.
  • Use of mocks or simple iterators to isolate the unit under test.

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