If you're interested in big models, LLM applications.OpenAI It is undoubtedly one of the most desirable companies. However, its technical interview process is also very "high threshold": not only examining the programming ability, but also emphasizing the depth of understanding of the principles of the model, engineering implementation. This article is based on the feedback of real candidates, organize the complete interview experience, help you save a lot of preparation time.
I. Overview of the interview process
| point | length of time | element |
|---|---|---|
| Resume Screening | - | Automated + manual, preferably AI projects & academic backgrounds |
| Recruiter Call | 30 minutes. | Motivation, background, values match |
| Technical Phone (1-2 rounds) | 45-60 minutes/round | Live coding, algorithms & data structures |
| Deep Dive Interview | 60 minutes. | Project Deep Dive + System Design or Modeling Principles |
| Research / ML Understanding | 60 minutes. | Transformer, training optimization, etc. |
| Final Round (VO) | Half a day - 1 day | Coding, Behavior, Take-home |
| Reference Check & Offer | - | Referrer verification |
II. Sample questions
1. Coding (90 minutes)
Title:Implement a simple database with in-memory support for SQL operations, supporting SELECT, WHERE, GROUP BY, ORDER BY, and JOIN in that order.
Key Thought:
- Define clear input formats to simplify parsing.
- Use Map/TreeMap to store table data, and implement each functional module step by step.
- Handwrite test cases to make sure each step is correct.
2. System design (60 minutes)
Title:Design a multi-tenant CI/CD scheduling system that receives repository IDs + commits, parses YAML definitions, and returns execution status in real time.
| module (in software) | design point |
|---|---|
| build | Multi-tenant isolation, high availability load balancing |
| data stream | API→MQ→Execution engine→State storage→Front-end push |
| stockpile | Redis/MongoDB state storage, Kafka decoupling |
| Permissions & Segregation | Tenant segregation, prevention of information leakage |
| connector | Query logs, status updates, task retries |
III. Behavioral Focus
- Self-directed learning vs. teamwork: examples of solving problems independently;
- Dealing with conflict: how to communicate with team disagreements;
- Ethical perceptions: attitudes toward ethically questionable projects;
IV. Trainee cases
Background:CS PhD, NLP & multimodal research, lack of systems engineering experience.
Results:Through VO escorting, real problem training, and architecture exercise, we were successfully awarded OpenAI Offer.
V. Description of services
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