Research Scientist applicants have rated the interview process at OpenAI with 3.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 60% positive. To compare, the company-average is 38.4% positive. This is according to Glassdoor user ratings.
Candidates applying for Research Scientist roles take an average of 9 days to get hired, when considering 10 user submitted interviews for this role. To compare, the hiring process at OpenAI overall takes an average of 30 days.
Common stages of the interview process at OpenAI as a Research Scientist according to 10 Glassdoor interviews include:
One on one interview: 50%
Phone interview: 25%
IQ intelligence test: 25%
Here are the most commonly searched roles for interview reports -
It was a really fun interview process! I got four interviews, and there was a final round after that. The problems asked were interesting and challenging and I had a great time. I'm excited to apply again soon!
I applied through an employee referral. The process took 3 days. I interviewed at OpenAI (San Francisco, CA) in Mar 2026
Interview
This was the first coding round after the HR call. It began with a self-introduction, followed by the coding portion, where I worked through the problem and explained my thinking step by step.
1. Coding & Algorithms
Expect standard algorithm and data structure problems (like from LeetCode).
Emphasis on clean code, optimal solutions, and reasoning.
Examples:
Implement a cache with O(1) access.
Design a rate limiter.
Solve graph traversal or dynamic programming problems.
2. Systems Design / ML Systems
Design robust, scalable systems—often in AI/ML contexts.
Examples:
How would you design a distributed training system?
How do you deploy and monitor a large language model in production?
3. Machine Learning & Deep Learning (for relevant roles)
Deep understanding of models (transformers, diffusion models, etc.)
Expect questions about training dynamics, loss functions, and optimization.
Examples:
Why does layer normalization work better than batch norm in transformers?
How would you debug a model that's overfitting?
Interview questions [1]
Question 1
Design a user-facing product powered by GPT-4.
How would you prioritize safety and utility in a new feature?