For North American international students eyeing the AI field, OpenAI is the ultimate dream employer. This tech titan, renowned for its groundbreaking work (e.g., ChatGPT), has a highly-coveted hiring process. Having guided many through the OpenAI interview, we’re sharing key insights to help you land that coveted offer.
The Interview Process of OpenAI
Application and Resume Screening
After submitting your application and resume, the recruitment team will typically review them within a week. If your materials stand out, you’ll proceed to the next stage.
Recruiter Phone Screen
This 30-minute call focuses on your background and motivation. Expect questions like “Tell me about yourself” and “Why OpenAI?” Prep by reviewing OpenAI’s research and culture to show alignment.
Technical Screen or Hiring Manager Call
Technical roles: A 1-hour online coding test (easy to medium) on platforms like Codility or HackerRank, assessing data structures and algorithms (DP, graphs, sorting, etc.).
Non-technical roles: A 30–60 minute video call with the hiring manager on domain knowledge, past experiences, and problem-solving skills.
On-site or Video Interviews
In the final stage, candidates face 3–6 rounds (30 min each) either on-site in San Francisco or via video. Interviewers vary by role:
- Behavioral: Assess mission alignment and collaboration. Use STAR to answer questions about resolving technical disagreements.
- Technical: Engineers do coding/data structures; researchers dive into published work and future research; system designers build end-to-end ML systems (e.g., recommendation engines, sentiment pipelines).
Classic Interview Questions of OpenAI
I. Coding
Given a binary sequence of 0s and 1s, convert it into standard musical note durations (whole, half, quarter, eighth, sixteenth). “1” marks the onset; consecutive “0”s continue the note. Use sixteenth note (1/16 beat) as the minimum unit. Decompose each run into standard durations.
II. Machine Learning Theory
- Attention Mechanism: Explain Transformer attention math and how it addresses RNN limitations on long sequences.
- Gradients: Describe causes of vanishing/exploding gradients and three mitigation techniques for each.
III. System Design
- Design a ChatGPT-like dialogue system: data collection, model training (framework, compute), deployment (high concurrency), security/privacy safeguards.
- Design a distributed AI training platform for massive neural nets: multi-node parallelism, fault tolerance, scalability—describe architecture and components.
IV. Behavioral
- Describe resolving a serious technical disagreement on a team project: communication, resolution, outcome.
- Share working under high pressure/uncertainty: mindset adjustment and ensuring quality and progress.
- Give an example of proactively learning a new AI technology and applying it successfully.
Final Passing Secret: Break Into OpenAI
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