Openai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Got a scheduling problem for an ML Engineer role at OpenAI that was more of a combinatorics puzzle than anything ML-related. Took me a while to even understand what the constraints were asking before I could start coding.

Questions Asked (1)

Design a data labeling task scheduler that takes t tasks, m models, and h human labelers, and returns a schedule as a list of (task, model, human) tuples. The schedule must ensure each human participates in at least k tasks total, that at any prefix of the schedule the per-(task, model) and per-(task, human) assignment counts stay balanced (max minus min no greater than 1), and that no human labels the same task more than once.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The constraint about prefixes is what got me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a greedy round-robin scheduling algorithm that assigns tasks to models and humans in a balanced way. Explain how to enforce the constraints using data structures like heaps or queues, and discuss trade-offs between simplicity and optimality.

Pro tip: Emphasize that the balance constraint is per (task, model) and (task, human) pair, not global, so you need to track counts per pair and ensure fairness at every prefix. Also, mention that the human participation constraint (at least k tasks) might require a second pass or a different assignment strategy if the greedy approach doesn't guarantee it.

1. Clarify requirements and edge cases

Ask questions to confirm the meaning of 'balanced' (max-min ≤ 1 at any prefix), whether tasks can be assigned to multiple models/humans, and if k is per human or total. Discuss edge cases like t=0, m=0, h=0, or k > t.

2. Design a greedy scheduling algorithm

Propose a round-robin approach: for each task, assign models and humans in a cyclic manner, ensuring that for each (task, model) and (task, human) pair, the counts differ by at most 1. Use a priority queue or counters to track assignments.

3. Enforce human participation constraint

After initial assignment, check if each human has at least k tasks. If not, reassign tasks from humans with excess to those with deficit, while maintaining balance constraints. Alternatively, incorporate the constraint into the greedy algorithm by prioritizing humans with fewer assignments.

4. Analyze complexity and trade-offs

Discuss time and space complexity of the proposed algorithm (e.g., O(t*m*h) naive, or O(t*(m+h)) with efficient data structures). Mention trade-offs between optimality and simplicity, and whether the greedy approach always satisfies all constraints.

5. Test with examples and validate

Walk through a small example (e.g., t=2, m=2, h=2, k=1) to demonstrate the schedule and verify constraints. Discuss potential failure cases and how to handle them.

Key Points to Mention

Definition of balance: for each (task, model) and (task, human) pair, the number of assignments at any prefix differs by at most 1.
Greedy round-robin assignment to achieve balance, using counters or queues to track next available model/human.
Ensuring each human gets at least k tasks: may require a separate pass or a modified greedy strategy that prioritizes under-assigned humans.
Constraint that no human labels the same task more than once: enforce by tracking (task, human) pairs and skipping if already assigned.
Complexity analysis: naive O(t*m*h) vs. optimized O(t*(m+h)) using heaps or balanced trees.
Edge cases: t=0, m=0, h=0, k > t, or when m or h is 1, and how the algorithm handles them.

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