# Software Engineer
**Role:** Software Engineer · **Level:** L5
**Company:** [OpenAI](https://scaleengineer.com/companies/openai)
**Difficulty:** Hard
**Salary:** US$180000 - US$250000
**Experience:** 5 - 10
**Timeline:** ~14 days
This interview process is designed to assess candidates for the Software Engineer (L5) role at OpenAI. It evaluates technical proficiency, problem-solving skills, system design capabilities, and cultural fit within OpenAI's innovative and collaborative environment.
Canonical: https://scaleengineer.com/interviews/openai/l5-software-engineer
---
## Overall evaluation

- Technical Skills
- System Design
- Behavioral and Cultural Fit

## Questions asked

- Given a stream of user activity logs, design a system to detect fraudulent behavior in real-time.
- Implement a function to find the k-th largest element in an unsorted array.
- How would you design a distributed cache system for a popular website?
- Describe a time you had a conflict with a teammate and how you resolved it.
- What are the trade-offs between SQL and NoSQL databases for a specific use case?
- Write a function to reverse a linked list.
- How would you design a system to handle millions of concurrent WebSocket connections?
- Tell me about a challenging technical problem you faced and how you overcame it.
- Explain the concept of eventual consistency and when it's appropriate to use.
- Design an API for a ride-sharing service.

## Preparation tips

### lists

- Thoroughly review fundamental data structures and algorithms.
- Practice coding problems on platforms like LeetCode (focus on Medium/Hard).
- Study system design concepts, including scalability, availability, and consistency.
- Prepare to discuss past projects in detail, focusing on your contributions and technical decisions.
- Understand OpenAI's mission, research, and products.
- Practice behavioral questions using the STAR method.
- Be ready to articulate your thought process clearly during problem-solving.

### studyPlan

- {"title":"Data Structures and Algorithms","longDescription":"Weeks 1-2: Focus on core data structures (arrays, linked lists, trees, graphs, hash tables) and algorithms (sorting, searching, dynamic programming, graph traversal). Practice implementing these from scratch and analyze their time/space complexity. Solve at least 20-30 problems per week.","shortDescription":"Weeks 1-2: Data Structures & Algorithms fundamentals. Practice 40-60 problems."}
- {"title":"System Design","longDescription":"Weeks 3-4: Dive into system design. Study common patterns like load balancing, caching, database sharding, message queues, and distributed transactions. Review case studies of large-scale systems. Practice designing systems for common scenarios (e.g., Twitter feed, URL shortener).","shortDescription":"Weeks 3-4: System Design principles and case studies. Practice designing 5-7 systems."}
- {"title":"Behavioral Preparation","longDescription":"Week 5: Prepare for behavioral interviews. Reflect on your past experiences, identify key projects, and prepare stories using the STAR method. Understand OpenAI's values and how your experiences align. Research common behavioral questions related to teamwork, problem-solving, and leadership.","shortDescription":"Week 5: Behavioral preparation. Prepare 10-15 STAR stories."}
- {"title":"Final Preparation","longDescription":"Week 6: Mock interviews and final review. Conduct mock interviews with peers or mentors focusing on both technical and behavioral aspects. Review any weak areas identified during practice. Ensure you can clearly articulate your thought process and solutions.","shortDescription":"Week 6: Mock interviews and final review."}

## Location differences

- {"location":"San Francisco Bay Area","differences":{"tips":["Research OpenAI's presence and projects in the specific region.","Be prepared to discuss how your experience aligns with regional technical needs.","Familiarize yourself with any relevant local tech communities or initiatives."],"interviewFocus":["Understanding of regional technical infrastructure and challenges.","Awareness of local market trends and user behavior.","Experience with region-specific compliance and data regulations."],"commonQuestions":["Discuss a challenging distributed system you designed or worked on.","How would you handle scaling a service to millions of users in a specific region?","What are the key considerations for data privacy and security in your region?","Describe your experience with local regulatory compliance related to technology."]}}
- {"location":"Seattle","differences":{"tips":["Review OpenAI's research papers and recent projects.","Be ready to discuss your contributions to significant AI/ML projects.","Practice explaining complex technical concepts clearly and concisely."],"interviewFocus":["Deep understanding of AI/ML principles and applications.","Proficiency in distributed systems and cloud computing.","Experience with large-scale data processing and machine learning pipelines."],"commonQuestions":["How do you approach building scalable AI/ML infrastructure?","Describe your experience with cloud-native architectures and Kubernetes.","What are the trade-offs between different machine learning frameworks?","How do you ensure the reliability and performance of large-scale data pipelines?"]}}

## Round 1: Technical Coding Round 1
**Type:** Data Structures and Algorithms Interview · **Difficulty:** Hard · **Duration:** 60 min
Coding challenges to assess fundamental CS skills.
This round focuses on your core computer science fundamentals. You will be presented with coding challenges that require you to apply knowledge of data structures and algorithms to solve problems efficiently. The interviewer will assess your ability to analyze problems, devise solutions, write clean and correct code, and explain your reasoning.
**Interviewers look for:** Strong problem-solving skills; Proficiency in coding; Understanding of algorithmic complexity; Ability to write clean, efficient code
**Evaluation criteria:** Correctness of the solution; Efficiency (time and space complexity); Code clarity and maintainability; Ability to handle edge cases; Communication of approach
**Common rejection reasons:** Inability to articulate thought process clearly.; Fundamental misunderstanding of data structures or algorithms.; Poor code quality or efficiency.; Failure to consider edge cases.
## Questions

- Implement a function to find the k-th largest element in an unsorted array.
- Given a binary tree, find its inorder traversal.
- Design a data structure that supports insert, delete, and getRandom O(1) operations.

## Preparation tips

- Practice coding problems regularly.
- Focus on understanding the 'why' behind different data structures and algorithms.
- Be prepared to explain your time and space complexity analysis.
- Practice coding on a whiteboard or in a shared editor without an IDE's help.

## Round 2: System Design Round
**Type:** System Design Interview · **Difficulty:** Hard · **Duration:** 60 min
Design a scalable and reliable system.
This round evaluates your ability to design complex, scalable, and reliable systems. You'll be given an open-ended problem and asked to design a system from scratch. The focus is on your architectural choices, understanding of trade-offs, and ability to handle various constraints like scale, latency, and availability.
**Interviewers look for:** Experience designing and building large-scale systems; Understanding of distributed system concepts; Ability to think critically about trade-offs; Pragmatic approach to problem-solving
**Evaluation criteria:** Scalability of the design; Reliability and fault tolerance; Performance considerations; Trade-off analysis; Clarity of communication
**Common rejection reasons:** Lack of understanding of distributed system principles.; Inability to handle scale and performance requirements.; Poor trade-off analysis.; Overly simplistic or complex designs without justification.
## Questions

- Design a URL shortening service like bit.ly.
- How would you design a system to handle millions of concurrent WebSocket connections?
- Design a distributed rate limiter.

## Preparation tips

- Study common system design patterns (e.g., microservices, caching, load balancing).
- Understand CAP theorem and its implications.
- Practice designing systems for common use cases.
- Be prepared to discuss databases, APIs, and communication protocols.

## Round 3: Behavioral and Managerial Round
**Type:** Behavioral Interview · **Difficulty:** Medium · **Duration:** 45 min
Assess behavioral competencies and cultural fit.
This round focuses on your behavioral and cultural fit. You'll be asked questions about your past experiences, how you handle challenges, work in teams, and your motivations. The goal is to understand how you operate, learn, and contribute to a collaborative environment.
**Interviewers look for:** Collaboration potential; Growth mindset; Resilience; Passion for OpenAI's mission; Ability to handle ambiguity
**Evaluation criteria:** Communication skills; Teamwork and collaboration; Problem-solving approach; Adaptability and learning; Alignment with OpenAI's values
**Common rejection reasons:** Poor communication skills.; Lack of self-awareness.; Inability to provide specific examples.; Poor cultural fit or misalignment with company values.
## Questions

- Tell me about a time you failed and what you learned from it.
- Describe a situation where you had to work with a difficult colleague.
- Why are you interested in working at OpenAI?

## Preparation tips

- Prepare specific examples using the STAR method.
- Reflect on your strengths and weaknesses.
- Understand OpenAI's mission and values.
- Be ready to discuss your career goals and why you're interested in OpenAI.

## Round 4: Final Round
**Type:** Final/Executive Round · **Difficulty:** Medium · **Duration:** 45 min
Discuss career goals and strategic alignment.
This final round often involves a senior leader who will discuss your overall experience, career aspirations, and how you can contribute to OpenAI's broader goals. It's an opportunity to ask high-level questions and ensure alignment on expectations.
**Interviewers look for:** Ability to lead technical initiatives; Mentorship capabilities; Understanding of product and business goals; Proactive problem identification
**Evaluation criteria:** Technical depth and breadth; Strategic thinking; Communication and influence; Leadership potential; Alignment with team goals
**Common rejection reasons:** Inability to connect technical skills with business impact.; Lack of strategic thinking.; Poor communication of technical vision.; Misalignment on role expectations.
## Questions

- How do you stay updated with the latest advancements in AI and machine learning?
- Describe a time you had to influence a decision that was not initially popular.
- What are your expectations for this role and how do you see yourself growing here?

## Preparation tips

- Think about your long-term career goals.
- Prepare questions about the team's roadmap and challenges.
- Be ready to discuss your leadership style and experience.
- Reiterate your passion for OpenAI's mission.