You're probably thinking about the math. Most people do. They sit down, open a LeetCode tab, and start grinding out dynamic programming problems until their eyes bleed. But if you’re aiming for a spot at the company that basically kickstarted the current global obsession with LLMs, the Open AI interview process is a completely different beast. It is less about "Can you invert a binary tree?" and much more about "How do you think when the ground is shifting under your feet?"
Honestly, it’s intense.
OpenAI isn't Google. They don't have 100,000 employees. Even in 2026, they keep their teams relatively lean compared to the old-school tech giants. This means every single hire is scrutinized for "research intuition" and a weirdly specific type of engineering pragmatism. You've got to be a bit of a polymath. If you're a pure researcher who can't write production-grade code, you'll struggle. If you're a brilliant coder who doesn't understand the underlying loss functions or why a model is hallucinating, you're probably not getting the offer.
The Initial Filter: It’s Not Just a Resume
The first step is usually a recruiter call, but don't let the casual tone fool you. They are looking for a very specific alignment with their mission. You'll likely talk to someone like a technical recruiter who has seen thousands of top-tier profiles. They want to know why you care about AGI. If your answer sounds like a PR script, they’ll know. They want to hear about the time you broke a model and figured out why.
Usually, this is followed by a technical screen.
Depending on the role—whether it's Research, Applied, or Infrastructure—this could be a coding challenge or a deep dive into a previous project. For Engineering roles, expect something practical. They might ask you to implement a specific component of a transformer architecture from scratch or optimize a distributed training bottleneck. It’s not about memorizing syntax; it's about demonstrating that you understand how data flows through a cluster.
The Core of the Open AI Interview Process
Once you pass the gatekeepers, you hit the "Onsite," which is now mostly virtual but no less grueling. This is where the Open AI interview process separates the enthusiasts from the experts. You’ll typically face five to six rounds.
One of the most famous (and feared) rounds is the Research Taste interview.
This isn't a coding test. It’s a conversation. An interviewer might present a hypothetical scenario: "We are seeing this specific degradation in model performance when we scale the context window. How do you investigate this?" They aren't looking for a "correct" answer because, in many cases, there isn't one yet. They want to see your process. Do you suggest checking the positional embeddings? Do you look at hardware constraints? Do you propose a small-scale ablation study?
They value people who can think from first principles.
Breaking Down the Rounds
- The Coding/System Design Hybrid: Most companies separate these. OpenAI often blends them. You might be asked to design a system that handles inference for millions of concurrent users while keeping latency under a specific millisecond threshold.
- The ML Theory Deep Dive: Expect to get grilled on the fundamentals. Why do we use Adam optimization? What happens if you change the initialization scheme? You need to know the "why" behind the "how."
- The Culture and Mission Fit: This is often done by a long-time employee or even a lead. They want to see if you’re actually okay with the "stochastic" nature of the work. Things change fast there. A project you work on today might be obsolete by Tuesday because of a new breakthrough.
The "Work Sample" Approach
One thing that makes the Open AI interview process unique is their occasional use of "Work Samples." Instead of just talking about what you could do, they give you a problem that mimics the actual day-to-day work. This could be a take-home or a live "collaborative" session.
✨ Don't miss: Layers of the Geosphere: What's Actually Happening Under Your Feet
They want to see how you handle feedback.
If an interviewer suggests your approach might be inefficient, don't get defensive. They are testing how you'd be in a research meeting. If you can pivot, acknowledge the flaw, and iterate on the fly, you’re winning. If you dig your heels in to protect your ego, you're toast. Sam Altman has often hinted in various interviews and talks that the company looks for "high agency" individuals—people who don't wait for instructions but find the most important thing to fix and just do it.
Why People Fail (Even the Smart Ones)
I've seen incredibly brilliant PhDs from Stanford and MIT strike out. Why? Usually, it's one of two things: lack of "engineering excellence" or a narrow specialization.
OpenAI needs people who can bridge the gap.
If you are a Research Scientist, you still need to be able to navigate a massive codebase. If you are a Software Engineer, you need to understand the nuances of backpropagation. Many candidates fail because they treat the Open AI interview process like a standard Big Tech interview. You can't just memorize the "Cracking the Coding Interview" book and expect to sail through. You need to be reading the latest papers from NeurIPS and ICML, but also knowing how to debug a Cuda kernel.
The Offer and the "Equity" Conversation
If you make it through the gauntlet, the offer stage is its own world. OpenAI has a unique compensation structure. For a long time, they used "PPUs" (Profit Participation Units) rather than traditional stock options, though their corporate structure has become more "standard" lately as they move toward a for-profit model. You need to understand the valuation. In 2026, with the company valued in the hundreds of billions, the upside is different than it was in 2020. It's less about "getting in early" and more about being part of the most influential tech company of the decade.
Actionable Steps for Candidates
If you're serious about this, don't just "study." Build.
First, go deep on a specific niche. Are you the world's best at low-precision training? Or are you a master of RLHF? Pick a lane but keep your generalist skills sharp. Second, contribute to open-source ML projects or replicate a paper from scratch. Don't just use a library—rebuild the logic. Third, practice explaining complex ML concepts to someone who isn't an expert. Clarity of thought is a massive signal for them.
Refine your "Research Taste" by reading papers and then looking at the code implementations. Ask yourself why the authors chose one hyperparameter over another. If you can't explain the trade-offs, you aren't ready.
Finally, get comfortable with ambiguity. The Open AI interview process is designed to be uncomfortable. It mimics the reality of working at the edge of human knowledge. If you can stay calm when you don't know the answer, and logically work your way toward a solution, you'll stand out more than the person who just has the right equations memorized.
Start by auditing your current projects. If they look like everyone else's GitHub, change them. Solve a problem that doesn't have a tutorial on YouTube. That’s what they are looking for.