OpenAI Software Engineer Interview Guide: Process, Coding, and System Design Questions

OpenAI software engineer interview questions often reflect the scale, complexity, and mission-driven nature of the company’s work. As OpenAI rapidly expands across software development, engineering, and infrastructure, you are stepping into a moment of historic growth. With nearly one billion users and $10 billion in annualized recurring revenue as of mid-2025, the demand for engineers who can build scalable, resilient systems has never been higher. OpenAI’s $40 billion funding round signals long-term confidence in its technology and team. As a candidate, you are not just applying to write code. You are preparing to shape the future of AI, collaborate with world-class talent, and help build the tools that support OpenAI’s bold path toward AGI.

Role Overview & Culture

As a software engineer at OpenAI, you will be deeply involved in designing and shipping the infrastructure and tools that power products used by hundreds of millions. You will collaborate daily with researchers, designers, and product teams, helping to integrate state-of-the-art AI models into real-world systems. You might build backend services in Python using FastAPI, work on distributed data pipelines in Azure, or use Codex to automate test writing. OpenAI engineers participate in on-call rotations, contribute to safety and compliance, and drive innovations across AI tooling and system reliability. This is not a traditional engineering role. You will be using AI to build AI, solving some of the world’s most complex technical challenges while shaping how AGI is safely deployed.

Why This Role at OpenAI?

If you are aiming for top-tier compensation, career-defining projects, and access to the most advanced AI infrastructure on the planet, OpenAI is hard to beat. As a software engineer, you could earn a base salary that’s 40 to 50 percent higher than at Google or Meta. The median total compensation sits around $875,000, thanks to generous performance bonuses and profit participation units that are tax-efficient and tied directly to OpenAI’s growth. Add in world-class benefits, unlimited PTO, and daily meals, and the package is as competitive as it gets. You will also collaborate with leading minds in AI, using state-of-the-art tools to build future-defining systems. Before you dive into practicing OpenAI software engineer interview questions, here’s how the process works.

The OpenAI software engineer interview process is structured, fast-paced, and designed to assess how well you can think, build, and collaborate in a mission-driven environment. While it varies slightly by team, most candidates will follow this general flow:

• Application Submission
• Recruiter Screen
• Technical Screen – Coding or HackerRank
• Virtual On-Site Loop
• Offer Stage & Timeline

Application Submission

When you submit your application, take the time to show how your background connects with OpenAI’s technical needs. Highlight projects involving distributed systems, data infrastructure, or machine learning tools. Upload a detailed résumé and include links to your GitHub, portfolio, or any relevant technical publications. Recruiters usually review applications within a week, although it can take longer if there’s high volume. Referrals are prioritized, so reaching out to someone inside OpenAI can help ensure your profile is reviewed. This stage is all about showing you’ve worked on complex, large-scale systems and that your experience translates to the challenges OpenAI is solving.

Recruiter Screen

In your 30-minute conversation with a recruiter, you’ll have the chance to talk about your background and motivations. This isn’t a technical round, but it’s important. You’ll be asked why you want to join OpenAI, what interests you about AI, and how your past work aligns with the company’s mission. You should be ready to share real examples of working on AI-driven systems or distributed architectures. The recruiter will also walk you through the next steps and answer your questions about teams, growth paths, and culture. It’s a great chance for both sides to get a feel for fit and alignment.

Technical Screen – Coding or HackerRank

This is where things get hands-on. You’ll complete a 1-hour coding interview, often via CoderPad or occasionally through a HackerRank-style platform. Expect practical problems that reflect real work at OpenAI, like managing versioned data stores or building efficient time-based queries. If you get a HackerRank test, it might also include math questions involving linear algebra or statistics. Python is preferred for these sessions, although you can use other languages if needed. Interviewers want to see how you approach problems, write clean code, and handle follow-ups. In some cases, you may be asked to complete a second technical screen depending on your performance or the role.

Virtual On-Site Loop

Your virtual on-site will span 4 to 6 hours over one or two days. You’ll meet with several team members and take on three types of interviews: live coding, system design, and behavioral. In live coding, you’ll solve complex problems that build in difficulty. During the design portion, you’ll architect systems like ML pipelines or real-time data processors, often in the context of OpenAI’s massive scale. Behavioral interviews will dig into how you collaborate, handle uncertainty, and embody the mission. You might also walk through a past project or research effort. OpenAI’s virtual format preserves the depth of traditional on-sites but adapts it for its globally distributed teams.

Offer Stage & Timeline

After the final round, the hiring team usually takes about a week to reach a decision. They’ll compile feedback from every interviewer to ensure a balanced view. If you’re selected, you’ll receive a detailed offer package that includes base salary, PPUs, bonuses, and elite benefits. Offers from OpenAI are often far above industry averages, with a median comp for engineers around $875,000. You can use this stage to ask clarifying questions, negotiate, and discuss what your role would look like in the long term. From start to finish, senior-level candidates may take up to four months to complete the process. For others, it typically wraps up much sooner.

If you’re preparing for the OpenAI interview, understanding the types of questions you’ll face is essential—expect coding, design, and behavioral challenges that reflect OpenAI’s real-world engineering problems.

Coding / Technical Questions

OpenAI coding interview questions tend to focus on building efficient, practical solutions using core data structures, clean logic, and real-world problem-solving strategies:

1. Given a dictionary with weights, write a function random_key that returns a key at random with a probability proportional to the weights.

To solve this, construct a list where each key appears a number of times equal to its weight. Use Python’s random.choice() to select a key from this list, ensuring the selection probability is proportional to the weights.

2. Write a function to rotate an array by 90 degrees in the clockwise direction

You can achieve a 90-degree clockwise rotation by transposing the matrix (switching rows and columns) and then reversing the order of its columns. The rotate_matrix function employs NumPy’s transpose and fliplr methods to perform these steps efficiently.

3. Given a list of integers, write a function to convert each integer to its Roman numeral representation

To solve this, create a dictionary mapping Roman numeral symbols to their values. Use a helper function to convert each integer to its Roman numeral by iterating through the dictionary in descending order of values, appending symbols while subtracting their values from the integer. Apply this helper function to each integer in the list to generate the Roman numeral representations.

4. Write a Python function called max_profit that takes a list of integers and returns the maximum profit achievable with at most two buy/sell transactions.

To solve this, iterate through the list of prices while maintaining four variables: buy1, profit1, buy2, and profit2. buy1 tracks the lowest price for the first transaction, and profit1 calculates the profit from the first transaction. Similarly, buy2 adjusts for the profit from the first transaction, and profit2 calculates the maximum profit achievable with two transactions. Return profit2 as the result.

5. Given a table of bank transactions, write a query to get the last transaction for each day

To solve this, you can use a ROW_NUMBER() function with a PARTITION BY clause to group transactions by date and order them in descending order of created_at. Then, filter for rows where the row number is 1, which corresponds to the last transaction of each day. Alternatively, you can use a subquery to find the maximum created_at for each day and filter the main table based on this result. Both approaches are efficient, but the first is more flexible for retrieving multiple transactions per day.

6. Build a k Nearest Neighbors classification model from scratch

To solve this, calculate the Euclidean distance between the new point and all data points, sort the data by distance, and find the mode of the target values of the k nearest neighbors. If there’s a tie, reduce k and repeat until a unique classification is found.

System Design Questions

OpenAI system design interview questions will test your ability to architect scalable, distributed systems—think production-ready services, not whiteboard theory—with challenges that often mimic internal engineering needs at massive scale:

7. Design a database schema for a Yelp-like system

To design the database schema for a Yelp-like system, create three main tables: user, restaurants, and reviews. The user table stores user profiles, the restaurants table stores restaurant information, and the reviews table stores user reviews. Use primary keys (id) for unique identification and foreign keys (restaurant_id and user_id) to establish relationships between tables. This schema ensures efficient data normalization and supports the application’s main functions.

8. Design a database to represent a Tinder-style dating app

To design a Tinder-style app, create tables for users, swipes, messages, and locations. Optimize the database by indexing the locations table for faster geospatial queries and consider sharding the swipes table due to its large size. Alternatively, use a streaming service to log swipes for scalability and analytics.

9. What would the table schema look like if we wanted to track how long each car took coming into San Francisco to enter and exit the bridge?

To model the schema, create a crossings table with columns for id, license_plate, enter_time, exit_time, and car_model_id. Additionally, create a car_models table with columns for id and model_name. Use a foreign key relationship between car_model_id in crossings and id in car_models to save space and represent car models efficiently.

10. Design a dynamic real-time dashboard for McDonald’s branch performance

To design the system, divide it into three sections: data source, processing engine, and dashboard. Use POS systems for real-time data acquisition, a processing engine with in-memory storage for updates, and a Kafka cluster for data streaming. Ensure reliability with failover mechanisms and load balancing, and use Redis for storing sorted sets to update the leaderboard every few seconds.

Behavioral & Mission-Alignment Questions

Beyond technical skills, OpenAI looks for engineers who align with its mission and values, so expect questions that explore how you collaborate, communicate, and make responsible decisions:

11. Why Do You Want to Work With Us

For an OpenAI software engineering role, your answer should reflect a strong alignment with the company’s mission to ensure that artificial general intelligence benefits all of humanity. It is important to show that you are not just interested in cutting-edge AI research, but also in the ethical and social implications of that work. Speak to OpenAI’s open research culture, collaborative environment, and focus on long-term impact. You can also mention specific projects like ChatGPT, Codex, or their open-source initiatives, and describe how your skills and values support their goals. If relevant, you might highlight your interest in safety, interpretability, or scalable deployment of AI systems.

12. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

At OpenAI, stakeholders may include researchers, policy teams, product managers, and external partners. Use an example where you had to bridge a gap between technical depth and strategic goals. For instance, you might describe a time when you needed to explain model behavior to someone without a deep technical background. Explain how you recognized the disconnect and adapted by using concrete examples, analogies, or visual aids. Emphasize the importance of empathy and active listening. OpenAI values cross-functional communication, so demonstrate that you can build mutual understanding across diverse teams.

13. How would you convey insights and the methods you use to a non-technical audience?

OpenAI engineers often present work to policymakers, educators, and product users who may not have a technical background. In your answer, discuss how you break down complex systems like transformers or fine-tuning techniques into understandable pieces. Mention using analogies, step-by-step storytelling, or interactive demos to bridge the knowledge gap. Also, highlight your awareness of potential concerns, such as bias or misuse, and how you address them when explaining AI systems. This shows that you not only communicate clearly but also responsibly.

14. How do you prioritize multiple deadlines?

Software engineering at OpenAI can involve overlapping priorities, such as shipping features, debugging infrastructure, and reviewing safety implications. Describe how you approach prioritization by evaluating impact, alignment with mission goals, and potential risks. You might mention working closely with cross-functional teams to clarify what matters most and adjusting timelines based on emerging needs. Show that you use a mix of planning tools, frequent check-ins, and iterative progress to stay on track. It is also helpful to highlight any experience managing ambiguity or working in fast-paced environments, since OpenAI moves quickly while maintaining a high bar for quality.

Preparing for a software engineering role at OpenAI means getting ready for one of the most challenging and rewarding interview experiences in tech. The bar is high, but with focused preparation, you can confidently show up ready to solve meaningful problems at scale.

First, master your coding fundamentals. Many OpenAI coding questions focus on practical applications of data structures, algorithms, and real-world systems. You should be comfortable writing clean, testable code under time pressure, especially in Python.

Next, mock the full loop of an OpenAI SWE interview. Practice moving through each phase—coding, system design, and behavioral—back to back. This helps build mental endurance and simulates the pace of the actual interview day.

For system design, go beyond textbook solutions. Deep dive into how distributed systems, data pipelines, and real-time APIs work at scale. Review the earlier design section to align your prep with OpenAI’s infrastructure needs.

During interviews, communicate clearly and think aloud. OpenAI values not just the final answer but how you reason through problems. Talk through trade-offs, constraints, and design decisions as you go.

Finally, include our AI Interviewer and HackerRank in your study plan, especially for medium to hard problems. While the OpenAI interview LeetCode style is more practical, these platforms sharpen your fluency and speed.

With smart preparation, you’ll be ready to take on technical challenges and demonstrate how your skills can help OpenAI build the future of AI.

Cracking the OpenAI software engineer interview is absolutely within reach—with the right strategy, preparation, and mindset. When you practice intentionally with real OpenAI software engineer interview questions, you not only sharpen your skills but also build confidence for each round. Curious how others succeeded? Read Jeffrey Li’s success story. Want a structured path to prepare? Explore our step-by-step OpenAI Python learning plan. Ready to drill the most common and challenging questions? Browse our full OpenAI basic SQL question bank or book a coaching session to practice under pressure. You’ve got this—let’s get to work.