Collecting OpenAI Interview Questions - 2025 Edition 
Let’s crowdsource our collective knowledge about OpenAI’s interview process
Why This Matters
OpenAI is scaling rapidly (projected $12.7B revenue, aiming for 1B users) and is one of the most competitive interview processes in tech. With median total compensation around $875K (40-50% higher than Google/Meta), it’s worth understanding their process.
What We Know So Far (2025)
Interview Structure
- Duration: 4-6 hours across 1-2 days
- People: 4-6 different team members
- Experience rating: 36.1% positive (3.17/5 difficulty)
- Timeline: Average 30.36 days, can be 6-8 weeks total
Process Breakdown
- Recruiter Screen (30-45 min) - Resume walkthrough, OpenAI research familiarity
- Technical Assessment - Pair coding, take-home, or HackerRank
- Technical Interviews - Practical algorithms (not pure LeetCode)
- Final Onsite/Virtual - The marathon round
Common Question Categories
Most Common: LRU Cache Implementation
“Solve the LRU cache problem” is THE most frequent coding question for SWE candidates.
Why it matters:
- Tests data structures (hashmap + doubly linked list)
- O(1) get/put operations
- Real-world caching scenarios
- Code quality and optimization
System Design Questions
- “Design ChatGPT” (obviously)
- “Design an LLM-powered enterprise search system”
- “Design GitHub Actions from scratch”
- “Design Yelp/Twitter/notification system”
Evaluation criteria:
- Scalability (horizontal scaling, sharding, load balancing)
- Reliability & fault tolerance
- Performance (latency for real-time responses)
- AI Infrastructure knowledge (GPU/TPU usage, distributed training, model versioning)
ML Engineering Specifics
- Model architectures and training methodologies
- Gradient descent optimization
- Recent research papers in your domain
- Data preprocessing pipelines
- Feature engineering challenges
- Model deployment considerations
- Reinforcement Learning from Human Feedback (RLHF)
Mission Alignment & Ethics
- “Why OpenAI?” - Show understanding of their mission
- AI safety and alignment research
- Responsible deployment approaches
- Ethics in AI development
Sample Questions from the Wild
Coding
# Classic LRU Cache with twist
class LRUCache:
def __init__(self, capacity: int):
# Implement with OrderedDict or custom doubly linked list
pass
def get(self, key: int) -> int:
# O(1) retrieval + move to front
pass
def put(self, key: int, value: int) -> None:
# O(1) insertion + eviction if needed
pass
System Design
- “How would you scale ChatGPT to handle 100M concurrent users?”
- “Design a system for real-time model A/B testing”
- “Implement GPU credit transactions for API usage”
ML Engineering
- “Walk through an experiment you designed to test a hypothesis”
- “How would you improve factuality in language models?”
- “Explain your approach to distributed training synchronization”
Preparation Strategy
Technical
- Master LRU Cache - Multiple implementations (OrderedDict, custom linked list)
- System design fundamentals - Focus on ML infrastructure
- Read OpenAI’s blog and research papers
- Understand RLHF, alignment research, safety interventions
Behavioral
- Know their charter and mission
- Have opinions on AI ethics and safety
- Prepare examples of real-world problem-solving
- Show genuine interest in responsible AI development
The Meta-Question
“OpenAI questions focus on deep reasoning, real-world problem-solving, and mission alignment—not trick questions or puzzles.”
What We Need from YOU 
Have you interviewed at OpenAI recently? Please share:
- Role you interviewed for (SWE, MLE, Research Scientist, etc.)
- Specific questions you remember
- What surprised you about the process?
- Preparation tips that actually helped
- Red flags or gotchas to avoid
Interviewing soon? Let us know:
- What role?
- What are you most worried about?
- How can we help you prepare?
Even if you didn’t get the offer, your experience helps everyone. This is a judgment-free zone for sharing intel.
Recent Updates (Sept 2025)
- Research focus shifting toward o3/o4-mini models, agentic systems (Deep Research, Operator)
- Safety emphasis increasing - more questions about alignment and responsible deployment
- Infrastructure scaling - questions about handling billion-user scale
- Product integration - ChatGPT, DALL-E, API ecosystem knowledge valued
Ground Rules
Share specific questions and experiences
Help others prepare with tips and resources
Discuss interview process and timeline
Ask for help with specific preparation areas
Don’t share internal/confidential information
Don’t disparage anyone who didn’t get offers
No self-promotion or recruiting
Let’s build the most comprehensive OpenAI interview resource on the internet. Who’s going first? 
Updated: September 2025 | Sources: Glassdoor, InterviewQuery, IGotAnOffer, OpenAI Blog