10 OpenAI Interview Questions

OpenAI is a non-profit company working within artificial intelligence (AI) and machine learning (ML) research and development. This laboratory has pioneered research in AI and focuses on creating safe artificial general intelligence (AGI) systems that can support human advancement. 

From having proficiency with coding to experience with machine learning and a passion for the advancements in robotics and AI ethics, OpenAI's broad spectrum of work demands a diverse skill set from its candidates. Understanding the types of questions you might face while interviewing with OpenAI and their underlying purposes can enhance your preparedness and make the interview process go smoothly. 

Reviewing potential OpenAI interview questions and preparing your approach to answering them can help you walk into your meeting with added confidence. Whether you're a software engineer, a data scientist, or an AI ethicist, these questions can help assess your technical expertise, problem-solving approach, creativity, and alignment with OpenAI's mission to ensure AI benefits all of humanity.

Question 1: Can you explain transformer architecture and its significance in models like GPT-4?

What they’re really asking: Confirmation that you have an understanding of one of OpenAI's influential and attention-grabbing projects.

How to answer the question: Transformer architecture enables ChatGPT to function by helping it process sentences and apply context using a self-attention mechanism. It also speeds up training and allows you to use a larger volume of training data.

Discuss the key components of the transformer model, such as its deep learning network and its impact on natural language understanding and generation. Make sure to mention the significance of its 175 billion machine learning parameters.

Other forms this question might take: 

• How do transformer models differ from previous neural network architectures? 

• What makes GPT-4 a significant advancement? 

Question 2: How would you approach an AI-related ethical dilemma?

What they’re really asking: What is your understanding of the various complexities of AI, and how do you navigate the technical landscape?

How to answer the question: Explain the major areas of ethical concern involving AI, such as lack of government oversight, privacy issues, and lack of human judgment. Outline the approach you take to ensuring you make ethically-minded decisions, emphasizing the importance of transparency, fairness, and stakeholder engagement.

Other forms this question might take: 

• Can you give an example of an ethical issue in AI and how you would resolve it? 

• How do you ensure AI models are free from bias?

Question 3: Describe a project where you solved a real-world challenge using machine learning.

What they’re really asking: Do you have practical experience in applying AI and ML techniques and delivering impactful solutions? How to answer the question: You could discuss specific concepts, such as classification models and reinforcement learning techniques, before detailing a specific project. Highlight the problem you were attempting to solve, the approach you took and technologies you used, and the outcome. Make sure to be specific on what machine learning model you used, like facial recognition, email automation, or social media optimization.

Other forms this question might take: 

• What challenges did you face while working on a machine learning project, and how did you overcome them? 

• How do you measure the success of an AI project?

Question 4: How do you stay current on emerging advancements in AI and ML? 

What they’re really asking: Are you committed to the continuous learning necessary for working in this field? How do you keep up with AI and machine learning research?

How to answer the question: Discuss the specific resources you rely on. For example, the OpenAI blog is one of several blogs that help keep advancements and ethical considerations at the forefront of AI discussions. Other resources include academic journals like the Journal of Machine Learning Research, online courses, conferences, academic journals, and professional networks you belong to or follow. Also, provide insight into how you implement what you learn into your work. Other forms this question might take: 

• Can you discuss a recent article you’ve read and the information you gained from it? 

• How do you incorporate new AI techniques into your current projects?

Question 5: Explain how you would optimize a machine learning model’s performance.

What they’re really asking for: Confirmation that you have the knowledge and expertise to enhance model efficiency, accuracy and scalability. 

How to answer the question: OpenAI measures progress using efficiency as a marker, which is why it’s essential you come prepared to discuss techniques like hyperparameter tuning, feature engineering, data improvement, regularization, and cross-validation. Include specific examples of when you’ve successfully optimized a machine learning model’s performance. 

Other forms this question might take: 

• What tactics would you use to improve machine learning model performance? 

• How do you balance model complexity with computational efficiency? 

• What metrics do you use to evaluate models’ performance?

Question 6: How would you handle missing or corrupt data in a data set?

What they’re really asking: How sharp are your problem-solving skills in preprocessing data and ensuring data quality? How to answer the question: Missing or corrupt data makes it challenging to analyze data or train algorithms properly. Outline various methods such as deleting rows when working with large data sets and data that won’t impact the outcome, imputation or filling in the blanks with reasonable estimates, creating a category for missing values, or using algorithms to predict and replace corrupt data, providing rationale for your choices. Other forms this question might take: 

• What are your preferred data cleaning techniques? 

• How do you assess your data’s integrity before using it for training a model? 

• How do you determine why data is missing?

Question 7: Describe your experience with collaborative projects in AI.

What they’re really asking: What is your experience with collaborating and working within a team? 

How to answer the question: Part of the interviewer’s job is to figure out how well you’ll fit with the company’s culture, which includes assessing your ability to work as part of a team. Share experiences where you worked as part of or with teams, highlighting the role you played, outcomes, and how you handled any conflict that may have arisen. Other forms this question might take:

• Can you describe a collaborative project you are proud of?

• Explain how you’ve handled conflict during the collaborative process.

• How would you handle having ideas that could advance your team’s progress? 

Question 8: How do you approach debugging a complex AI model?

What they’re really asking for: Insight on your approach to identifying and resolving issues. How to answer the question: Detail your process for troubleshooting AI systems, including important factors such as robustness, reliability, stability, and resilience. Be sure to include the tools and techniques you use for monitoring model performance and identifying anomalies. Other forms this question might take: 

• What's your strategy for ensuring AI model robustness? 

• Describe a time when you’ve encountered an unexpected model output and how you addressed it. 

Question 9: How would you approach implementing users’ feedback to optimize AI and ML models? 

What they’re really asking: Do you value feedback as a tool for refining models and how do you approach the process?

How to answer the question: Describe your approach, which may include analyzing all available feedback, defining common issues and patterns, and assess the feasability of making appropriate changes. You may also explain how you prioritize multiple changes and tools like A/B testing that you might use to determine if proposed changes will create the desired solution. Other forms this question might take: 

• How do you use feedback to improve your work? 

• What tools or methods do you use to ensure improvements create the intended impact?

Question 10: What excites you about working with OpenAI and how do you align with our mission?

What they’re really asking: What is your motivation for joining OpenAI and your commitment to its ethical and humanitarian objectives? How to answer the question: Interviewers want to know how you’ll fit into the company and if your goals and interests line up with their mission. Talk about the value you could offer in helping with ML system design challenges or coding issues, Express your enthusiasm for AI's potential to benefit humanity and consider citing specific OpenAI projects, research, and initiatives and how you see yourself contributing. Describe how you relate to OpenAI’s mission to ensure that AGI plays a beneficial role that’s advantageous for humanity at large. Other forms this question might take: 

• How do you see yourself contributing to OpenAI's mission? 

• What aspects of OpenAI's work are you most passionate about?

Prepare for OpenAI interview questions with Coursera 

OpenAI attracts a competitive pool of talent, and its hiring managers often focus on candidates’ ability to use data and research to solve problems. Anticipating possible interview questions and practicing your answers can help you best present your skills and knowledge during your OpenAI interview.

You can also prepare with online courses and programs such as Stanford and DeepLearning.AI’s Machine Learning Specialization. Other possible options to build your skill set and foundational knowledge include courses like Edureka’s Generative AI Foundations and IBM’s Generative AI: Impact, Considerations, and Ethical Issues. You’ll find these options and more on Coursera.