Scale Engineer

Software Engineer

Software EngineerL6Hard

This interview process is designed to assess candidates for the Software Engineer (L6) role at OpenAI. It evaluates technical expertise, problem-solving abilities, system design skills, and cultural fit.

Rounds·4

Timeline·~14d

Experience·6 - 10 yrs

Comp band·US$180000 - US$250000

Interview time·210 min

Evaluation

What they measure.

  • Technical depth and breadth in software engineering principles.
  • Problem-solving skills and analytical thinking.
  • System design capabilities, focusing on scalability, reliability, and maintainability.
  • Coding proficiency and best practices.
  • Communication and collaboration skills.
  • Leadership potential and mentorship abilities.
  • Alignment with OpenAI's mission and values, including ethical considerations.

Preparation

How to prepare.

Tips

  1. Review fundamental computer science concepts: data structures, algorithms, operating systems, and databases.
  2. Practice coding problems on platforms like LeetCode, focusing on medium to hard difficulty.
  3. Study system design principles and common architectural patterns (e.g., microservices, load balancing, caching).
  4. Prepare to discuss your past projects in detail, highlighting your contributions and technical challenges.
  5. Research OpenAI's mission, products, and recent publications.
  6. Think about how your skills and experience align with the specific requirements of the L6 role.
  7. Prepare questions to ask the interviewers about the role, team, and company culture.

Study plan

Fig · Study plan — 04 phases

01 / 04
01

Phase 01 of 04

Foundational Computer Science

Weeks 1-2: Data Structures & Algorithms (DSA) fundamentals, OS basics.

Weeks 1-2: Focus on core data structures (arrays, linked lists, trees, graphs, hash maps) and algorithms (sorting, searching, dynamic programming, graph traversal). Practice implementing these efficiently and analyze their time/space complexity. Cover fundamental OS concepts like processes, threads, memory management, and concurrency.

Questions

Commonly asked.

  • Design a URL shortening service.
  • How would you design a system like Twitter's feed?
  • Implement a function to find the k-th largest element in an unsorted array.
  • Describe a challenging bug you encountered and how you debugged it.
  • Tell me about a time you disagreed with a teammate or manager. How did you handle it?
  • How would you design a distributed cache?
  • What are the trade-offs between SQL and NoSQL databases?
  • Explain the concept of eventual consistency.
  • How do you approach performance optimization in a large-scale system?
  • Describe a project where you had to make significant technical decisions with incomplete information.

Locations

Regional differences.

Fig · Regions — 02 locations

01 / 02

Location

San Francisco Bay Area

Interview focus

Deep dive into distributed systems and scalability relevant to AI infrastructure.Understanding of large-scale data processing and machine learning pipelines.Leadership and mentorship capabilities.Ethical considerations in AI.

Common questions

  • Discuss a challenging distributed system you designed and the trade-offs you made.
  • How would you design a system to handle real-time data processing for millions of users?
  • Describe a time you had to mentor junior engineers. What was your approach?
  • What are your thoughts on the ethical implications of AI development in your specific domain?

Tips

  • Be prepared to discuss your experience with cloud platforms (AWS, GCP, Azure) and their services relevant to AI.
  • Familiarize yourself with common AI/ML frameworks and libraries.
  • Highlight instances where you've influenced technical direction or mentored teams.
  • Articulate your perspective on responsible AI development.

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.