Key insights from analyzing 50+ OpenAI interview reports: 1) They test coding speed AND quality - expect to write production-ready code in 45 minutes. 2) Math derivations are common - be ready to derive attention, loss functions, optimization algorithms on the whiteboard. 3) ‘Scale it 10x’ is their favorite follow-up - always think about scaling constraints. 4) They ask about failure modes for EVERYTHING - ‘What breaks first when this system is overloaded?’ 5) Recent trend: questions about AI agents working together. This is becoming a major focus area.
Mobile dev interview update: Got asked to ‘build a UI using Jetpack Compose’ during tech screening. Not just basic UI - they wanted me to implement custom animations, state management, and performance optimizations. Also: ‘Design a mobile chat interface that gracefully handles AI response streaming with 3+ second delays.’ They care a lot about user experience during AI interactions - loading states, partial responses, error handling, offline scenarios.
Recent ML Engineering questions: ‘Approach fine-tuning a model to reduce harmful outputs while maintaining performance on benign tasks.’ ‘How would you design a data pipeline to detect and filter training data contamination?’ ‘Implement a simple reinforcement learning setup to train a model to play tic-tac-toe from scratch.’ Also got behavioral: ‘Describe your experience with reinforcement learning and how it applies to alignment research.’ They really want to see RLHF understanding.
‘Refactor a bad code’ was one of my coding questions - they gave me 200 lines of Python with security vulnerabilities, performance issues, and maintainability problems. Had to identify issues and fix them systematically. Another: ‘Design a system to detect if generated code contains potential security vulnerabilities.’ More behavioral than usual: ‘How would you handle discovering a potential safety issue in a deployed model that millions use daily?’ Ethics and safety questions are INTENSE.
Product strategy deep-dive: ‘OpenAI has 3,500+ employees and 0 billion revenue. How would you structure the product org to maintain innovation velocity while ensuring safety?’ ‘Design the rollout strategy for a new AI capability that could disrupt a major industry.’ ‘How do you balance user demand for powerful features vs responsible deployment timelines?’ They want you to think like a VP - not just feature prioritization but industry-level impact.
Enterprise search system design (recent question): ‘Support natural language queries like What is the revenue for Q1 2025?’ and ‘Provide answers as summaries or direct excerpts from documents.’ Had to design indexing, query processing, result ranking, and citation tracking. Follow-up: ‘How would you scale this to handle 100M documents while maintaining sub-second response times?’ They really focus on LLM-powered systems that work at enterprise scale.
2024-2025 interview trend analysis: 1) ‘Practical over LeetCode’ - actual work problems, not algorithmic puzzles. 2) Every system design now assumes LLM integration. 3) Safety/ethics questions are 30-40% of behavioral time. 4) They test code quality obsessively - error handling, testing, documentation. 5) Recent pattern: ‘How does this change with GPT-5?’ type scaling questions. 6) New: questions about AI agents coordinating on tasks. The bar keeps rising - they want ‘coding machines’ who also think about societal impact.
UX/Frontend focus questions: ‘Design an interface where users can iteratively refine AI-generated content through conversation.’ ‘How would you handle showing confidence levels for AI responses without overwhelming users?’ Coding: ‘Implement a React component that gracefully handles streaming text responses with typing indicators.’ They care deeply about making AI interactions feel natural and trustworthy. Also asked about accessibility - ‘How do you make AI-powered interfaces usable for users with disabilities?’
Leadership behavioral trends: ‘How do you build a team culture that prioritizes both innovation and safety?’ ‘Walk through how you’d onboard 1000 new engineers while maintaining code quality and safety standards.’ ‘Design a technical review process for AI capabilities that could impact millions of users.’ Recent addition: ‘How do you structure engineering teams when your product capabilities evolve every 6 months?’ They want leaders who can scale while maintaining OpenAI’s culture and safety focus.
Specific coding question from my August 2025 interview: ‘Largest Local Values in a Matrix’ but with a twist - instead of fixed 3x3 submatrices, they wanted variable k×k size. Had to find largest values in every contiguous k×k submatrix. Best solution is O(n²log n) time complexity. They really tested edge cases: what if k > n? What if matrix is empty? Code quality was crucial - helper functions, error handling, clean logic separation.
Code debugging/refactoring round details: They gave me a 150-line Python function with multiple issues - memory leaks, SQL injection vulnerabilities, race conditions, inefficient algorithms, and poor error handling. 45 minutes to identify and fix everything. Then: ‘Now add unit tests and explain your testing strategy.’ The behavioral follow-up: ‘How would you prevent these issues in a code review process for AI model serving code?’ Very practical, not theoretical.
Applied statistics round (new addition to ML engineer track): ‘Given this dataset of model performance metrics, identify potential biases and statistical significance issues.’ Had to use Python/numpy to analyze A/B test results, identify Simpson’s paradox, and explain confidence intervals. ‘How would you design an experiment to test if GPT-4.5 is better than GPT-4 for code generation?’ Required knowledge of statistical testing, experimental design, and multiple comparisons problems.
Hard LeetCode-style question from recent senior engineer interview: ‘Design a data structure that supports insertion, deletion, and get-random in O(1) time, but also supports get-median in O(log n).’ Had to combine hashmap + balanced BST. Follow-up: ‘How would you distribute this across multiple machines while maintaining consistency?’ They wanted both algorithmic depth AND distributed systems knowledge. The bar is extremely high - you really need to be a ‘coding machine’ as they say.
Mobile-specific behavioral questions: ‘ChatGPT mobile app has 200M+ users. How would you roll out a major UI change without breaking user workflows?’ ‘Design the infrastructure for A/B testing UI changes at that scale.’ ‘How do you handle users on old app versions when you push model updates?’ Also technical: ‘Implement offline message queuing with conflict resolution for when connectivity returns.’ Mobile + AI scale is a unique challenge.
Product strategy case study: ‘A competitor just released an AI model that matches GPT-4 performance at 1/10th the cost. You have 48 hours to present a response strategy to the board.’ Had to consider pricing, product differentiation, R&D priorities, and competitive positioning. ‘How do you maintain product-market fit when the underlying technology changes every 6 months?’ These aren’t typical PM questions - they’re about navigating rapid AI evolution.
Leadership scenario questions: ‘You’re managing 5 teams building different AI capabilities. One team discovers their model has potential safety issues 2 weeks before a major product launch. Walk through your decision process.’ ‘How do you structure performance reviews for engineers working on capabilities that didn’t exist 12 months ago?’ ‘Design an incident response process for AI safety issues that could affect millions of users.’ Leadership at OpenAI means navigating unprecedented challenges.
Finance/business strategy deep-dive: ‘Model the unit economics for ChatGPT if inference costs drop by 50% but usage increases 10x.’ ‘How would you structure pricing for a new AI agent capability where usage patterns are completely unknown?’ ‘Design a financial model for OpenAI’s path to 0B revenue.’ Got into GPU procurement strategy, R&D investment timing, and margin analysis at scale. They want finance people who understand the technology stack.
Compilation of toughest questions reported from recent interviews: 1) ‘Implement a distributed consensus algorithm for model weight updates across 1000 GPUs.’ 2) ‘Debug this transformer implementation - there are 7 subtle bugs.’ 3) ‘Design a system where AI agents bid on compute resources in real-time.’ 4) ‘How would you implement Constitutional AI from mathematical foundations?’ 5) ‘Build a rate limiter that adapts based on user’s subscription tier and current system load.’ The technical bar has never been higher.
Data Scientist interview specifics (September 2025): ‘Create a function rain_days to calculate probability of rain on nth day after today’ - had to implement Markov chains and explain assumptions. ‘Given this dataset of model performance metrics across different demographics, identify potential biases.’ Technical: ‘Explain central limit theorem and why it’s important for A/B testing at OpenAI scale.’ Also system design: ‘Design an AI-powered search system for enterprise documents with natural language queries like What is the revenue for Q1 2025?’ Required LLM integration, vector embeddings, and ranking algorithms.
Technical Program Manager interview (August 2025): ‘Walk me through a large technical program you’ve led involving cross-functional teams.’ Follow-up: ‘How would you coordinate a product launch across Safety, Research, Product, and Legal teams when the underlying model capabilities are changing weekly?’ Scenario: ‘Website showing slow performance, mistake unnoticed until user reported to management - walk through your incident response.’ They want TPMs who can handle AI development velocity and uncertainty. Process took 6 weeks, 7 total interviews including technical presentation.