Openai·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Interviewed for a software engineering role at OpenAI and got a coding question about GPU credits. Short and focused, not much else to report.

Questions Asked (1)

Design or implement a system for tracking and managing GPU credit usage.

System DesignTechnical Trade-offs

Author's notes

This felt pretty domain-specific to OpenAI which made sense in retrospect but I wasn't expecting it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what is being tracked (GPU hours, credits, cost), who are the users (researchers, teams), and what scale (thousands of GPUs, millions of jobs). Then design a system that ingests usage events, aggregates them in near real-time, enforces quotas, and provides dashboards and alerts. Emphasize trade-offs between consistency, latency, and cost.

Pro tip: Show awareness of the unique challenges at OpenAI: multi-tenant research workloads, bursty usage, and the need for fair-share scheduling. Mention that credit systems must be auditable and support chargebacks to teams.

1. Clarify Requirements and Scope

Ask questions to understand what 'GPU credit' means (e.g., dollar cost, GPU-hours), who consumes it (individuals, teams, projects), and what actions are needed (tracking, alerting, enforcement). Define non-functional requirements like scale, latency, and accuracy.

2. High-Level Architecture

Propose a pipeline: usage events from GPU schedulers -> message queue (Kafka) -> stream processing (Flink/Spark Streaming) for aggregation -> time-series DB (Prometheus/InfluxDB) for metrics -> API and dashboard. Include a separate service for quota enforcement and alerting.

3. Data Model and Aggregation

Design schemas for usage events (user, team, job ID, GPU type, duration, credits) and aggregated views (per user/team daily/monthly). Discuss windowing (tumbling vs sliding) and how to handle late or duplicate events.

4. Quota Enforcement and Alerts

Explain how to enforce quotas: pre-check before job submission, periodic checks, or hard limits via scheduler integration. Describe alerting mechanisms (email, Slack) when thresholds are crossed, and how to handle overages (throttle, suspend, or allow with notification).

5. Trade-offs and Scalability

Discuss trade-offs: strong vs eventual consistency for credit balances, real-time vs batch processing, cost of storage vs query speed. Address scalability: partitioning by user/team, sharding, and using distributed counters (e.g., Redis) for fast reads.

Key Points to Mention

Idempotency and exactly-once processing to avoid double-counting credits.
Multi-tenancy and isolation: ensuring teams only see their own usage.
Integration with existing schedulers (e.g., Kubernetes, Slurm) and authentication systems.
Auditability: logging all credit changes for compliance and debugging.
Fair-share scheduling and priority: how credits influence job prioritization.
Cost efficiency: using spot instances or preemptible GPUs and reflecting that in credit calculation.

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