Officially confirmed
What OpenAI says about its process
- 1An introductory conversation covers your experience, motivations, goals, and fit with the role or team.
- 2Skills-based assessments vary by team and may include pair coding, take-home projects, technical tests, or more than one assessment.
- 3Final interviews typically span four to six hours with four to six people over one or two days, virtually by default with an onsite option in San Francisco.
- 4Engineering evaluation emphasizes well-designed solutions, high-quality code, performance, test coverage, communication, and collaboration.
Official sources
Last verified 2026-08-02. We update the page when official hiring information materially changes.
Role coverage
Roles this guide supports
Practice focus
Signals to rehearse
- Production-quality coding
- Distributed AI infrastructure
- LLM serving and evaluation
- Testing and performance
- Technical communication
Independent company-style practice
AI engineering scenarios relevant to OpenAI
These are original EngMock exercises based on public engineering themes. They are not represented as official or leaked OpenAI interview questions.
Design a Multi-Tenant LLM Inference Platform
Design a global inference service for mixed chat, coding, and long-context workloads across heterogeneous accelerators.
View scenario →Design a Shared GPU Cluster Scheduler
Design scheduling and capacity management for training, research, and production inference on a large heterogeneous accelerator fleet.
View scenario →Design an Evaluation Platform for AI Agents
Design datasets, trace grading, simulation, regression detection, and release gates for non-deterministic tool-using agents.
View scenario →Design a Fault-Tolerant Distributed LLM Training Platform
Design the training stack for a 100B+ parameter model across tens of thousands of accelerators.
View scenario →Design a Secure Cloud Coding Agent
Design isolated execution, permissions, credentials, networking, and auditability for an autonomous coding agent.
View scenario →Design a Multi-Agent Research System
Design an orchestrator-worker agent system for open-ended research with parallel search, synthesis, citations, and bounded cost.
View scenario →Practice the underlying interview skills
Combine a live technical mock with the candidate-reported Question Bank, then use the evidence-based report to choose your next focused scenario.