From Scheduling to Prefix Balance: An OpenAI Software Engineer Interview Case in 2026

Quick Answer

• OpenAI’s current guidance says engineering assessments vary by team and may include pair coding, technical tests, take-home projects, or other skills-based formats.
• In this reported Senior Software Engineer phone screen, the base task tested feasibility and constructive coding; the follow-up added prefix-balance invariants and made output order part of correctness.
• The candidate’s strongest move was clarifying constraints. The main gap was discovering the impossibility check too late and then struggling to formalize the follow-up under time pressure.

URL：https://www.screna.ai/experience/89f6beb6-742a-4fdf-97c1-e559ae70a118

Interview Snapshot

Where This Reported Round Fits

OpenAI’s official Interview Guide describes introductory conversations, role-specific skills-based assessments, and later final interviews, while explicitly noting that the experience can differ by role and team. For engineering interviews, the company says it generally looks for well-designed solutions, high-quality code, optimal performance, test coverage, communication, and collaboration. The case analyzed here was reported as a Senior Software Engineer Technical Phone Screen; it should not be treated as a fixed stage for every OpenAI engineering role.

A Real Senior OpenAI Software Engineer Technical Screen

The first prompt was:

Design a scheduling system for a data labeling platform with tasks, AI models, and human annotators. Given counts for each entity type and a minimum assignment threshold k, return a list of [task, model, human] assignments such that every human appears in at least k assignments and no human is assigned the same task more than once.

The candidate did something important well: they spent the first minutes clarifying constraints rather than coding immediately. They recognized that “any valid schedule” did not require optimization and identified the central impossibility condition: if k exceeds the number of tasks, one human cannot receive k assignments without repeating a task.

The weakness was timing. “I fumbled the impossibility check at first and had to backtrack after writing half my solution,” the candidate recalled. That backtracking is often preventable by stating feasibility first.

Screna mentor CodeWithMaya made the same point: “The impossibility check is genuinely the first thing that bites people here.” Once feasibility is established, the base construction is relatively mechanical: rotate through tasks and humans, assign models as needed, track per-human task usage, and stop after every human reaches k.

The reusable lesson is broader than this question: before writing the loop, state the capacity limit, uniqueness rule, termination condition, and invariant that must remain true after each assignment.

The Follow-Up Changed the Problem

The interviewer then asked:

Modify your solution to return a prefix-balanced schedule, where at every prefix of the output the task-model usage counts differ by at most 1, and each human's per-model usage counts also differ by at most 1.

This is where the candidate struggled. “I tried to think of it as interleaving sorted rounds but couldn't formalize it fast enough under pressure,” they recalled. The round-robin idea captured the intuition, but they did not reach a clean implementation and later considered a priority queue.

The follow-up changes what must be proven. The first prompt asks for a valid final schedule; the second imposes correctness on every prefix, making output order part of the algorithm.

Mentor Sarah Millstone summarized the shift: “The prefix-balance constraint flips this from a generation problem into an ordering problem.” Her stronger direction is to prioritize underrepresented task-model buckets while selecting humans in a way that preserves each human’s model balance and the no-repeat-task rule.

A Stronger Answer Structure

1. Separate the two invariants: global task-model balance and per-human model balance.
2. Define human eligibility: no repeated task and no model choice that creates imbalance greater than one.
3. Use a balanced selection structure, such as heaps with deterministic tie-breaking.
4. Update counts after every assignment and verify both invariants.
5. Test adversarial small cases before claiming correctness.

What This Case May Signal

This single case suggests that a Senior OpenAI Software Engineer screen may reward more than fast implementation. The follow-up exposed whether the candidate could recognize a new invariant, change abstractions, and explain why naive ordering no longer guarantees correctness. This is consistent with OpenAI’s public emphasis on solution quality, performance, testing, communication, and collaboration, but the evidence here remains one candidate report.

How to Prepare for Similar OpenAI SWE Questions

Practice constructive problems using a fixed sequence: constraints → feasibility → invariant → construction → complexity → edge cases → tests. Then add follow-ups involving fairness, prefix guarantees, or ordering.

For senior-level practice, do not stop at “round robin should work.” Explain what it guarantees, where it can fail, and what closes the gap. With Screna, an InterviewPrep Note can become a company-, role-, and round-specific AI mock, followed by feedback on structure, technical depth, and clarity, then mentor review.

FAQ

What should I expect in an OpenAI Software Engineer technical screen?

OpenAI says skills-based assessments vary by team and can include pair coding, technical tests, take-home work, or other formats. This article covers one candidate-reported phone screen.

Are OpenAI Software Engineer interviews only about algorithms?

No. OpenAI’s public guide also highlights solution design, code quality, performance, testing, communication, and collaboration.

How should a senior candidate handle a difficult coding follow-up?

Restate what changed, define the new invariant, explain why the original approach may fail, and only then choose a new data structure.

Does every OpenAI Software Engineer candidate get a scheduling question?

No such conclusion is supported here. This is one candidate-reported question, not a universal question set.