Scale Engineer

Software Engineer

Software EngineerL2Medium

This interview process is designed to assess candidates for a Software Engineer (L2) position at OpenAI. It evaluates technical proficiency, problem-solving skills, system design capabilities, and cultural fit.

Rounds·3

Timeline·~14d

Experience·2 - 5 yrs

Comp band·US$130000 - US$180000

Interview time·150 min

Evaluation

What they measure.

  • Problem-solving approach
  • Algorithmic knowledge
  • Data structure proficiency
  • Code quality and efficiency

Preparation

How to prepare.

Tips

  1. Review fundamental data structures and algorithms.
  2. Practice coding problems on platforms like LeetCode and HackerRank.
  3. Study system design concepts and common architectural patterns.
  4. Prepare to discuss past projects and technical challenges in detail.
  5. Research OpenAI's mission, values, and recent work.
  6. Practice behavioral questions using the STAR method.

Study plan

Fig · Study plan — 04 phases

01 / 04
01

Phase 01 of 04

Data Structures and Algorithms

Weeks 1-2: Data Structures & Algorithms fundamentals. Practice coding.

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 from scratch and analyze their time and space complexity.

Questions

Commonly asked.

  • Given an array of integers, find the contiguous subarray with the largest sum.
  • Design a system to handle millions of concurrent users for a social media platform.
  • How would you optimize a slow database query?
  • Describe a time you disagreed with a teammate and how you resolved it.
  • What are the trade-offs between SQL and NoSQL databases?
  • Explain the concept of recursion and provide an example.
  • How do you approach debugging a complex issue in a distributed system?

Locations

Regional differences.

Fig · Regions — 02 locations

01 / 02

Location

San Francisco

Interview focus

Deep understanding of distributed systems and scalability.Experience with large-scale data processing.Ability to articulate complex technical concepts clearly.

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 how you approached it.

Tips

  • Familiarize yourself with common distributed system design patterns.
  • Be prepared to discuss trade-offs in system design decisions.
  • Practice explaining your thought process for problem-solving.

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.