Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026

Summary

OpenAI SWE interview with an ML coding problem around active learning and classifier training. The original post was pretty sparse on details so a lot had to be inferred, but the core task involved building a pipeline to select unlabeled samples for human annotation and then training a classifier on the results.

Questions Asked (1)

Design and implement a workflow that selects unlabeled samples for human annotation, trains a classifier on the newly labeled data, and outputs evaluation results.

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

The problem statement they gave me was weirdly vague.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a modular pipeline with clear interfaces between sampling, annotation, training, and evaluation. Emphasize trade-offs (e.g., uncertainty vs. diversity sampling, batch size, retraining frequency) and discuss how you would measure success and iterate.

Pro tip: Propose a baseline first (e.g., random sampling) and then justify improvements with expected gains and costs. This shows pragmatism and helps you avoid over-engineering.

1. Clarify Requirements and Constraints

Ask about data volume, labeling budget, latency requirements, and evaluation metrics. Define success criteria and scope (e.g., single iteration vs. continuous loop).

2. Design the Pipeline Architecture

Outline components: unlabeled pool, sampling strategy, annotation interface, training module, and evaluation. Define data flow and storage between stages.

3. Choose Sampling Strategy

Select a method (e.g., uncertainty sampling, diversity sampling, or hybrid) based on goals. Discuss how to balance exploration and exploitation.

4. Implement Training and Evaluation

Train a classifier on labeled data, using appropriate validation. Evaluate on a held-out test set and compare against baselines (e.g., random sampling).

5. Iterate and Monitor

Set up a loop to repeat sampling and training, monitoring performance and cost. Discuss when to stop and how to handle drift.

Key Points to Mention

Active learning strategies (uncertainty, diversity, query-by-committee)
Handling class imbalance and annotation noise
Batch vs. sequential sampling and retraining frequency
Evaluation metrics (accuracy, F1, AUC) and statistical significance
Scalability and infrastructure (e.g., distributed training, annotation tooling)
Trade-offs between labeling cost, model performance, and latency

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