Scale Engineer

Software Engineer

Software EngineerL4Medium to Hard

This interview process is designed to assess candidates for the Software Engineer (L4) role at OpenAI. It evaluates technical proficiency, problem-solving skills, system design capabilities, and cultural fit.

Rounds·3

Timeline·~14d

Experience·3 - 7 yrs

Comp band·US$130000 - US$180000

Interview time·150 min

Evaluation

What they measure.

  • Problem-solving ability
  • Algorithmic thinking
  • Data structures knowledge
  • Code quality and efficiency
  • Debugging skills

Preparation

How to prepare.

Tips

  1. Review fundamental computer science concepts: data structures, algorithms, operating systems, databases.
  2. Practice coding problems on platforms like LeetCode, HackerRank, or AlgoExpert, focusing on medium to hard difficulty.
  3. Study system design principles and common architectural patterns.
  4. Prepare for behavioral questions by reflecting on your past experiences and using the STAR method.
  5. Research OpenAI's mission, values, and recent work.
  6. Understand the basics of machine learning and AI, especially if applying for roles with an AI focus.

Study plan

Fig · Study plan — 03 phases

01 / 03
01

Phase 01 of 03

Data Structures and Algorithms

Weeks 1-2: Data Structures & Algorithms (Arrays, Trees, Graphs, DP, Sorting, Searching).

Weeks 1-2: Focus on Data Structures and Algorithms. Cover arrays, linked lists, trees, graphs, hash tables, heaps, sorting, searching, dynamic programming, and graph traversal algorithms. Practice implementing these and analyzing their time and space complexity.

Questions

Commonly asked.

  • Given an array of integers, find the contiguous subarray with the largest sum.
  • Design a rate limiter.
  • Explain the difference between a process and a thread.
  • How would you design a system to handle millions of concurrent users?
  • Tell me about a time you disagreed with a teammate and how you resolved it.
  • What are your thoughts on the ethical implications of AI?
  • Implement a function to reverse a linked list.
  • Describe the trade-offs between using a relational database and a NoSQL database for a social media application.

Locations

Regional differences.

Fig · Regions — 02 locations

01 / 02

Location

San Francisco Bay Area

Interview focus

Deep understanding of distributed systems and scalability.Proficiency in large-scale data processing and analysis.Experience with machine learning infrastructure and MLOps.Ability to contribute to cutting-edge AI research and development.

Common questions

  • How would you design a URL shortener service?
  • Explain the CAP theorem and its implications for distributed systems.
  • Describe a challenging technical problem you solved and your approach.
  • How do you handle concurrency in your code?
  • What are your thoughts on the latest advancements in AI and their potential impact on software engineering?

Tips

  • Familiarize yourself with OpenAI's research papers and recent projects.
  • Be prepared to discuss your contributions to open-source AI projects.
  • Highlight any experience with large language models (LLMs) or generative AI.
  • Showcase your passion for AI and its ethical implications.

Rounds

Round-by-round.

Expand a step for evaluation criteria, sample questions, and prep notes.

DSA

Coding questions at OpenAI.

Frequently reported on OpenAI loops

View all at OpenAI

Other guides

More at OpenAI.