Inside OpenAI: How They Hire, Onboard, Promote, and Build Elite Engineering Teams

Insights on how OpenAI does hiring, onboarding, and growing their people. Based on my conversation with the Head of Engineering, ChatGPT. Building elite engineering teams is not just about hiring great engineers. It's about designing the systems that help them succeed: how you hire them, how quickly they get up to speed, and how they grow inside the company. OpenAI has developed their specific approach across all three. Their hiring process focuses on identifying strengths rather than forcing every candidate through identical interviews. New engineers are encouraged to ship code early and take ownership from day one. And growth inside the company happens continuously through feedback, impact, and strong judgment rather than fixed promotion cycles. I recently had the pleasure of speaking with Sulman Choudhry, Head of Engineering, ChatGPT. This is the second part of the 2-part article based on my conversation with Sulman. Make sure to also read the first part to learn about OpenAIs engineering culture: And, in this article, we take a closer look at how OpenAI hires, onboards, develops engineers, and what makes their engineering teams so effective. This is an article for paid subscribers, and here is the full index: - 1. OpenAI's hiring process - 3 things that make their hiring process different - They tailor their hiring process based on the candidate - A new type of interview that has been very successful for them - They prefer a mix of both very senior and very junior talent - 2. Growing inside OpenAI - No fixed cycles for promotions, instead they can happen anytime throughout the year 🔒 What do they look for when promoting an engineer to a lead role? 🔒 3. Onboarding process 🔒 They have a defined 30, 60, and 90-day plan 🔒 2 common mistakes engineers make when onboarding 🔒 4. Knowledge sharing 🔒 5. The difference between an average team and the elite engineering team 🔒 Last words Let's start! Their hiring process is fairly standard and similar to what many big tech companies do. It typically starts with a recruiter screening call, followed by technical and behavioral interviews, and then the offer stage. For more senior candidates, the process may also include additional steps such as executive conversations or follow-up interviews. The initial screening is almost always conducted remotely. On-site interviews are more common for senior candidates. And they find in-person interviews to be valuable because they give both the candidate and the team a better sense of fit. Executive conversations and later-stage interviews can take place either on-site or virtually. Overall, the process is fairly standard. In addition to traditional hiring, the company also brings in talent through acqui-hires and acquisitions of other companies. In those cases, the focus is not only on what teams are building but also on identifying exceptional individuals and maintaining a very high bar for talent. These 3 things make their hiring approach a bit different from other companies: 1. They are starting to test for AI literacy, though that's still in a pilot phase. 2. They intentionally design interviews to highlight candidates strengths rather than forcing everyone through the exact same process. 3. They encourage calculated risk-taking in hiring. They want recruiters and hiring managers to take thoughtful risks because those often lead to the best outcomes. And when a risk doesn't work out, they focus on accountability and learning from it. That mindset ultimately helps them build a very strong and high-density talent team. The part of the process Sulman feels strongly about is tailoring interviews to focus on people's strengths. When hiring processes are too strict and everyone goes through the exact same interviews, you can miss on strong candidates. Sulman mentioned a good example: Their iOS codebase is written in Swift and uses SwiftUI. However, many large tech companies still have older codebases built in Objective-C. Because of that, a lot of experienced iOS and macOS engineers are much stronger in Objective-C than in Swift. They realized they were losing good candidates because they were being evaluated only through Swift interviews. Some of them failed, not because they were weak engineers, but because they didn't yet have deep experience with Swift. So they changed the process. If someone has a strong Objective-C background, they allow them to interview using Objective-C. The goal is to evaluate people based on their strengths. At the end of the day, a good engineer can learn a new programming language quickly. In fact, some of the best iOS engineers they've hired recently are people who might have struggled in a Swift-only interview but performed very well in Objective-C. They recently introduced a new type of interview. They describe it as a combined product and technical deep dive. In this interview, candidates are given a problem that is mostly an AI-first, AI-native problem. They are asked to think about the problem and how they would approach building a product around it. There is no prework required. During the interview, the candidate and interviewers work through the problem together. They discuss what the product specification might look like and what kinds of systems and architectures would be needed to build it. This format has proven to be a very high signal for them. It helps evaluate a candidate's intuition about AI models, their ability to collaborate, and their engineering judgment when designing systems and solutions. Another benefit of this interview is that it works well across different roles. Because responsibilities across teams are becoming more blurred, other functions have also shown interest in using it. For example, product leadership is considering using the same format when interviewing product leaders. In recent years, many companies shifted toward hiring only senior engineers. But, they prefer a mix of both very senior and very junior talent. Because of that, they invest heavily in hiring new graduates and building a strong internship program. Many interns eventually join the company full-time, and new college graduates also make up an important part of the hiring pipeline. Another successful source of talent has been very early-stage startups. In many cases, small groups of recent graduates start building products together instead of joining large companies. These teams often bring strong motivation, creativity, and ownership, which have made them very successful hires. They also rely heavily on personal networks, especially for leadership roles. Leaders are expected to have a strong eye for talent and the ability to attract and motivate great engineers to join their teams. As a result, many engineers say one of the reasons they joined the company is because their friends are there, or some of the best people they've worked with are already there. That network effect has become an important driver of hiring for them. The philosophy around performance management is intentionally different from what is common in many large tech companies. At many companies, performance reviews happen once or twice a year, and a lot of time is spent preparing formal reviews, giving feedback, and making promotion decisions during those cycles. In contrast, their approach focuses on more continuous, real-time performance management. Formal review cycles sometimes become just a formality or are even canceled because feedback is already happening regularly. Managers spend time throughout the year aligning on expectations and calibrating performance. For example, they hold regular meetings with their leadership team to review people. In these meetings, they might discuss engineers who joined in the past 90 days and evaluate how they are performing, making sure they are receiving timely feedback and support. In other cases, they might review how the most senior engineers in the organization are doing. These conversations help managers stay aligned on performance and expectations across teams. Much of the performance management happens through regular one-on-one meetings and small calibration sessions rather than large formal reviews. If someone consistently demonstrates that they are operating at the next level, they can be promoted without waiting for a specific review period. The overall philosophy is to provide feedback and recognition as quickly as possible. Rather than relying heavily on large annual recognition programs, the focus is on understanding what matters to each person and recognizing their contributions close to the time the work was done. This creates a much tighter and more effective feedback loop for them. What do they look for when promoting an engineer to a lead role?...