OpenAI Interview: "First Principles" and Engineering Limits
If you're preparing for an interview at OpenAI, forget the traditional FAANG interview routine; OpenAI, a company at the forefront of artificial intelligence, has a different talent selection mechanism. It's not looking for a "problem-solving expert", nor is it looking for a "concept repeater", it is looking forPartners capable of solving mankind's unsolved engineering and scientific problems from "first principles"The
I've coached several engineers to successfully get OpenAI Offers, and their experiences have given me a deep understanding that OpenAI interviews are more like a conversation aboutDepth of thought, taste in engineering and identification with future missionsThe depth of the conversation. It requires you to understand not only the technology, but also the scientific principles and philosophical thinking behind it.
Today, I'm going to break down the unique logic of OpenAI interviews and share a set of preparation strategies that really work.
Table Of Content
Online Assessment (OA)
OpenAI's SDE (Software Development Engineer) Online Assessment (OA) is the first hurdle, but its style is very different from traditional algorithmic competitions in that it doesn't go for "fancy algorithmic showmanship", but is more oriented towards"Readings, Scenario Abstraction, and Engineering Realization."The
Many candidates flop here, not because the algorithms are too difficult, but because they are so used to the standard solutions in the question bank that they get bogged down in the details once they encounter questions that require abstracting complex scenarios into mathematical models or classic algorithms.
Take, for example, two classic questions that they often test:
1. Largest Non-Overlapping Subset
This is essentially a classic Interval Scheduling problem. The interviewer gives you a description of two arrays.startrespond in singingfinish, allowing you to pick the most non-overlapping events.
The question itself is an introductory question to the greedy algorithm, but it has the pitfalls ofboundary conditionrespond in singingCleanliness of the code. What the interviewer wants to see is: can you recognize at a glance that this is a greed problem, and can cleanly implement"Sort by end time, selecting the next earliest ending and non-overlapping event each time"This core logic.
The real difficulty is: have you considered whether "end time equals start time" is an overlap? In engineering practice, this usually means that it can be "picked up".Critical understanding of business rulesand in the realizationAvoid index clutterThe ability of the
2. Grouping Marbles
This puzzle requires you to move marbles of different colors with minimal manipulation so that each color forms a continuous segment.
Veteran Interpretation: At first glance, this looks like DP or complex graph theory, but it centers on "minimum number of moves".The nature of the math. The number of times you need to move to get color C to connect together is actuallyThe number of marbles of a non-C color, the final minimum number of operations, is the number ofTotal number of marbles minus the color with the highest number of marblesThe
OpenAI examines you in this waysimplify matters by making them complicatedThey want to know if you can quickly grasp the "first principles" of a problem in a complex scenario description. They want to know if you can quickly grasp the "first principles" of a problem in a complex scenario description - i.e.Translating problems into statistics or greedy choices. If you get into complex DP state transfers right off the bat, it's a sign that your abstraction skills and engineering intuition aren't mature enough.
Technical interviews (VO)
OpenAI's Video Interviews (VOs) are the real battlegrounds, which are usually a mix of System Design and Coding, and pursued in far greater depth than the usual big players.
The coding wheel: from data structures to persistence engineering
Their coding questions tend to be of the "looks simple, but Follow-up can dig you until you collapse" variety.
in order to"Designing a Simple Key-Value Store."As an example. Writeput/get/deleteJust warming up. The interviewer will follow up immediately:
"What would you do if the data needed to be persisted?" "If there is a 1KB limit per file and you have to write serialized data in chunks to multiple files, how do you design it?"
This is no longer an algorithmic question, butEngineering Realization Questions. What the interviewer wants to see is your understanding ofStorage, serialization, indexing and chunkingof understanding. You need to articulate clearly:
-Chunking: How to write data chunks to multiple files.
-Meta Index: How to record with a separate index fileKeycorrespondingDocument numberrespond in singingoffsetThe
-Startup Optimization: How to realizePartial Loadto avoid reading all files into memory at startup.
This kind of question asks you toThinking and expressing go hand in hand. You must clearly verbalize your architectural design and tradeoffs while writing code. Once your thoughts are incoherent, the interviewer's follow-up questions will leave you with nothing to hide.
System design: designing systems for the "future"
OpenAI's system design questions are often centered around theLarge-scale AI/ML infrastructuremaybecore productExpand.
Design Slack (or similar real-time communication system): The point of this question is not to draw a classic microservices architecture diagram, but toFan-out mechanismrespond in singingdata consistency. You need to discuss this in depth: in large-scale Channels, the choice of theFan-out on ReadneverthelessFan-out on Write? And clearly explain the two options inRead and write latency, storage cost and system scalabilityTrade-off on.
Design a Distributed AI Model Training Platform: This is one of the most hardcore exam questions you'll need to discussHyperscale parallel trainingThe architecture of theData Parallelism,Model Parallelism,Fault Tolerance, ** distributed storage (e.g., All-Reduce communication)as well asScheduling System (CI/CD Scheduler)** design. This requires you to have a deep understanding of the underlying principles of distributed computing and deep learning.
In the design session, never get bogged down in the realization details right off the bat. Take a minute firstOverview of the core components and data streamsand then take the initiative to guide the interviewer into"Weighing the trade-offs."The discussion. When you can say, "I choose option X, which sacrifices Y but guarantees Z," you've earned the interviewer's respect.
Algorithmic Depth: Steiner Tree and Graph State Search
In some OA or technical aspects of more research-oriented or core engineering positions, there will be what appear to be algorithmic competitions that look at theMathematical modeling skillsThe title.
For example, Optimal Path (optimal path on the tree)problem that requires you to find a shortest path through the tree to visit all the task nodes. This is essentiallyA simplified version of the minimally connected subtree (Steiner Tree). The interviewer wants to see if you can derive the core conclusion that the shortest path length is equal to2 * (sum of edge weights of smallest subtrees) - distance(start, end)The
another exampleMinimum Operations to Reach Target (modulo graph state search)This question is typical ofBFS Status Search. It examines whether you can take a seemingly complex math operation and abstract it into aThe shortest path problem on unweighted graphs, and efficiently resolved with standard BFS templates.
The final selection: values and mission
OpenAI's Behavioral interviews are the most "humane" but also the most rigorous part of the process. They don't ask you "how to resolve conflict", they ask you"How to live with the future of humanity"The
1. Ethics and sense of missionThey'll ask you what you think ofAGI (General Artificial Intelligence)views, and your perception of AI ethics. You need to demonstrate a deep identification with OpenAI's mission of"Ensuring Generalized Artificial Intelligence for the Benefit of All Mankind"The 2. Growth Mindset: they'll ask you howActive learning of new AI technologiesHowBreaking through technical bottlenecks. Your answer must reflect that you are aSelf-driven, curious, and able to learn quickly from failureThe people. 3. Conflict and collaboration: When describing disagreements with teammates, the point is not who is right or wrong, but how youResolve differences and move projects forward through communication and data analysis. They look for partners who can maintain a professional and collaborative spirit in a high-stress, high-uncertainty environment.
Deepen your foundation and embrace the unknown
OpenAI's interview, an interview for your tech career"Mitzvah.". It requires you to step out of your comfort zone and tackle cutting-edge challenges with the most solid foundation, theIf you are a recent graduate, focus your energies on theData Structures, Graph Theory, Fundamentals of Distributed Systemson the deep dives and try to apply them to ML/AI scenarios. If you're a senior engineer looking to make the jump, keep your focus on theDepth of system design and Trade-off analysison and be prepared to articulate your thoughts on where AI is headed in the future.
Forget the fancy tricks and get back to the "why."respond in singingThe essence of "how". When you can think clearly, derive rigorously, and articulate with reverence for the future like a true scientist and engineer, you'll be able to cut through the fog and become a part of OpenAI. Good luck with your interview!
