In the minds of North American studentsOpenAI OpenAI is the mecca for job hunting in the AI field. This tech giant, which leads the world with cutting-edge technology, hardcore projects and innovative culture, is famous for its phenomenal products such as ChatGPT, and its talent selection mechanism has attracted a lot of attention as well.We've received a few interviews with OpenAI, and we'd like to share some super practical OpenAI interview experience to help you get an offer!
OpenAI Interview Process
Application and Resume Screening
After submitting your application and resume, the hiring team will typically review it within a week. If the application materials are bright enough, you will be able to move on to the next stage.
Recruiter Phone Screen (RPS)
This is an approximately 30-minute phone interview with a recruiter. It focuses on your resume, personal background, and motivation for applying to OpenAI, with frequently asked questions such as "Tell me about yourself" and "Why do you want to work at OpenAI". Before the interview, it is recommended to learn about OpenAI's latest projects through its research blogs and news, so that your answers fully reflect your knowledge of the company and identification with its values.
Technical Screen or Hiring Manager Call
- technical postCoding Tests: Online coding tests of about 1 hour in length, of medium to easy difficulty, are usually organized on platforms such as Codility or Hackerrank. They are mostly conducted on platforms such as Codility or Hackerrank. They mainly examine coding skills and understanding of basic data structures and algorithms (e.g. dynamic programming, graph traversal, sorting problems, etc.).
- Non-technical positions: There may be a 30-60 minute video call with the department manager that focuses on your knowledge of the area related to the position, past experience, and problem solving skills.
On-site Interviews or Video Interviews (On-site Interviews)
This is the final stage of the interview process, with some candidates traveling to OpenAI's headquarters in San Francisco for 3-6 rounds of on-site interviews, and many using video interviews. Each round is about 30 minutes long and is conducted by a different team member. The structure of the interviews varies by position, but each position has a domain-specific assessment.
- behavioral interview: Focus on assessing how well you identify with the company's mission and your ability to work in a fast-paced, collaborative environment. Frequently asked questions such as "Describe your experience with a team project where you had a disagreement with a member, and how you resolved the disagreement and moved the project forward" are answered with the recommended use of the STAR rule.
- technical interview: The focus of the assessment varies from position to position. Engineering positions may involve algorithms and data structure issues, or even require on-site code writing; research positions have thesis results, and need to elaborate on the work content, self-reflection and future direction; system design positions may require the design of end-to-end ML systems, such as recommendation engines or real-time sentiment analysis pipelines, and so on.
Interview results and follow-up
Interview results are usually announced within a week of the final round of interviews. During this time, the recruiter may ask you to provide references. The entire interview process usually lasts about a month and may sometimes be extended.
OpenAI Interview Classic Questions
I. Coding
Given a string containing only 0 respond in singing 1 binary sequence of notes, which is required to be converted into a series of standard note tensors (whole note, half note, quarter note, eighth note, sixteenth note, etc.).
- A "1" indicates the onset of the note, followed by a "0" for sustain.
- In sixteenth notes (
A16) is the smallest time value unit: if there is only "1", it takes up 1 minimum unit (1/16 shot); "1 0" takes up 2 minimum units, and so on. - In the final output, the total time value corresponding to each "1" and subsequent consecutive "0" is decomposed into standard note time values.
II. Machine Learning Theory Questions
- theme: Explain in detail the mathematics of the attention mechanism in Transformer and show how it addresses the shortcomings of RNNs when dealing with long sequences.
- theme: Causes and at least three solutions for gradient vanishing and gradient explosion in deep learning.
III. System design questions
- theme: Design a dialog generation system similar to ChatGPT, with detailed design in terms of data collection, model training, service deployment, high concurrency, and privacy protection.
- themeDescribe the overall architecture and key components of a distributed AI model training platform for OpenAI that supports hyper-scale parallel training, fault tolerance, and scaling.
IV. Behavioral interview questions
- theme: Tell us about an experience where a disagreement with a teammate over a technical route was resolved through communication, and what was the outcome?
- theme: Share experiences of working in high-pressure, uncertain environments and how to ensure quality and progress?
- theme: Describe a case where a new AI technology was actively learned and successfully applied to a project.
The final secret to breaking into OpenAI
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