PayPal AI & Machine Learning Engineer Interview Guide (2025) – Process, Questions & Prep Tips

With an average PayPal account holder making 61 transactions each year, making the platform more convenient, safe, and fulfilling for shopping experiences has been a challenge. This was addressed in 2025 under CEO Alex Chriss and his vision for strategic transformation to “PayPal 2.0,” evolving from a payment provider into a comprehensive commerce platform.

Empowered by the new AI/ML vision, PayPal is now spearheading the agentic commerce market with its AI-powered tools to transact on the customer’s behalf, making it easier to send and receive complex payments. In partnership with AI and cloud leaders like Microsoft and AWS, PayPal AI/ML Engineers have also integrated AI across its infrastructure to enhance fraud detection, secure payments, and refine customer experiences.

Role Overview & Culture

An AI/ML Engineer at PayPal is part of the Enterprise AIML Solution team, tasked with revolutionizing commerce through personalized AI-driven experiences for over 400 million users. As an AI & Machine Learning Engineer at PayPal, you will be responsible for developing, optimizing, and fine-tuning generative AI and LLM solutions for various applications, including personalization initiatives taken recently. Deployment, monitoring, and maintaining PayPal’s MLOps platform would also be a critical part of your day-to-day. This also includes managing the end-to-end lifecycle of LLMs. You would also be able to work with a sophisticated tech stack, including Python, Spark, Java, Scala, SQL, and NoSQL, gaining a vast industrial experience and learning new tools. Cloud platforms are also a critical part of the AI/ML engineer job role. Furthermore, you’re likely to end up in a cross-functional team with business, technology, and data science departments working towards the same goal of project success, driving innovation, and delivering solutions.

Why This Role at PayPal?

Working at PayPal as an AI & Machine Learning Engineer offers the opportunity to work on high-impact projects at a massive scale, shaping the future of global commerce, especially when it has become the core pillar of PayPal’s growth strategy recently. As a PayPal machine learning engineer, you’ll work on sensitive systems that enhance personalization for nearly 400 million consumer accounts, manage risk for billions of transactions, and improve operational efficiency. The position demands a deep understanding of ML system design, cloud infrastructure, and NLP techniques, providing significant opportunities for professional development.

The PayPal interview experience itself reflects this, assessing candidates on DSA, ML concepts, system design, MLOps, and business impact.

If you’re preparing for an AI and Machine Learning Engineer role at PayPal, it’s important to understand their multi-stage interview process, which evaluates both your technical depth and your ability to work in cross-functional, real-world scenarios. Here’s how their typical steps go:

• Application & Recruiter Screen
• Technical Interviews
• Onsite or Full Loop Interviews
• Feedback & Hiring Committee

Application & Recruiter Screen

Despite the demand, the competition for the AI/ML engineer role at PayPal is still on the higher side. So, if you have a referral, use it to submit your application to increase your chances of getting noticed. While not having a referral may make securing an interview a tad bit harder, it’s still worth going through the PayPal career portal to apply for the AI/ML job role that suits your preferences. Whatever your method of application is, ensure your resume highlights proficiency in Python, SQL, and various ML algos, in addition to experience with data structures, stats, and system design. Past projects hold a lot of importance in PayPal interviews, so showcase projects that demonstrate your business impact and innovation.

If your resume is shortlisted, the next step is a 30-minute recruiter screen, either via call or a video call. This is your contact person from PayPal’s Talent Acquisition team who strives to verify your experience, assess your skills, and discuss your motivation for applying. While they may ask for a walkthrough of one of your projects, they aren’t part of the technical assessment teams.

Technical Interviews

For the AI/ML engineer role at PayPal, the technical interviews take a 2-step approach. While the first online assessment has taken a backseat in 2025 due to AI interventions, it’s sometimes still conducted to get a brief idea about your foundational skills before going into deeper problems. The OA component of the technical interviews usually includes DSA questions at a medium to hard difficulty level. SQL problems may also be included at this stage. In some cases, our candidates report receiving a take-home assignment focusing on ML model development, data analysis, and demonstrating proficiency with algorithms and hypothesis testing.

The second component of the technical interviews for the AI/ML engineering interview process at PayPal is the technical virtual interview round, which could progress with one or two, one-hour each, rounds of problem-solving and ML topics discussion.

Expect live coding exercises to evaluate your proficiency in Python and your ability to solve complex problems. You will also be expected to explain your thought process clearly while implementing solutions for DSA-focused challenges. The ML concepts and theory portion assessed your understanding of ML concepts like classifications, regression models, bias-variance tradeoff, and their practical applications. Don’t be surprised if you’re asked to write complex SQL queries as well.

Onsite or Full Loop Interviews

The onsite interview loop for a Machine Learning Engineer at PayPal is a multi-round process designed to thoroughly assess your technical skills, problem-solving abilities, and cultural fit. It typically consists of 4 to 6 interviews lasting between 3 and 5 hours, involving conversations with engineers, hiring managers, and cross-functional partners.

You can expect 2-3 DSA questions during the coding rounds of the loop, typically inching towards the hard difficulty levels, including topics like graphs, arrays, dynamic programming, and heaps. They’re interested in your approach more than the correct answer. While the correct answer is still important, talk through your approach before dry-running your code with test cases.

The onsite ML system design round is possibly the most critical part of the whole PayPal AI/ML engineer interview process. Expect open-ended questions requiring you to build systems from scratch. You might be asked to design a fraud detection model, a recommendation system, or an anomaly detection service. You will need to discuss the entire lifecycle, including data processing, model selection, deployment, and monitoring. The focus is on scalability, latency, cost-efficiency, and how the system would function in a production environment.

The MLOps round is often led by a hiring manager or a senior engineer, probing your practical experience with deploying and maintaining models. You’ll be asked to discuss your past projects in detail, explaining the business impact and technical challenges. It’s a more discussion-specific round, but you need to be aware of the topics like handling model drift, strategies for retraining, monitoring systems in production, and have experience with cloud platforms like AWS.

The concluding behavioral hiring manager round includes in-depth discussions about your past projects and behavioral questions about teamwork, handling challenges, and your motivations. For senior roles, a techno-managerial round may explore your experience with agile methodologies and handling product scenarios. At this stage, you should demonstrate alignment with PayPal’s values through examples from your experience.

Feedback & Hiring Committee

After the interviews, each interviewer submits feedback. The hiring committee then reviews all evaluations, discusses your performance, and makes a final decision. At this stage, references may be checked. The process is collaborative, aiming to ensure a fair and well-rounded hiring decision for the AI/ML role.

This section explores the types of questions you might encounter in a PayPal interview, spanning technical, system design, and behavioral topics relevant to AI and machine learning roles:

Coding / Technical Questions

You can expect hands-on questions that test your ability to write SQL queries, implement algorithms, and manipulate data structures for practical machine learning applications:

1. Implement gradient descent to calculate the parameters of a line of best fit

To solve this, you need to implement a gradient descent algorithm that iteratively adjusts the slope and y-intercept to minimize the error between the predicted and actual y-values. The process involves calculating the gradient of the error with respect to the slope and intercept, updating these parameters in the direction that reduces the error, and repeating until convergence.

2. Write a query to get the total three-day rolling average for deposits by day

To solve this, first filter the transactions to include only deposits, then aggregate them into a daily total. Use a self-join to combine each day’s total with the totals from the previous two days, and calculate the average of these three days.

3. Given a table with event logs, find the percentage of users that had at least one seven-day streak of visiting the same URL.

To solve this, use a series of Common Table Expressions (CTEs) to first extract unique login dates for each user and URL. Then, calculate the streaks by grouping consecutive dates and identify users with streaks of at least seven days. Finally, compute the percentage of such users by dividing the count of users with streaks by the total number of users, rounding the result to two decimal places.

4. Given a table of subscriptions, write a query to get the retention rate of each monthly cohort for each plan_id for the three months after sign-up.

To calculate the retention rate, create a series of common table expressions (CTEs) to process the subscription data. First, extract the start month from the start date, then generate a cross join to create a row for each of the first three months. Finally, calculate the retention rate by checking if the subscription end date is beyond the current month being considered, and aggregate the results to get the retention rate for each cohort and plan.

5. Given a JSON string with nested objects, write a function that flattens all the objects to a single key-value dictionary

To flatten a JSON string with nested objects, convert the JSON string to a Python dictionary and recursively traverse it. For each nested object, concatenate the keys with an underscore and store the result in a new dictionary. Finally, convert the flattened dictionary back to a JSON string.

System / Product Design Questions

These questions focus on your ability to design scalable, intelligent systems that align with real-world use cases like recommendation engines, fraud detection, and content moderation:

6. How would you build a machine learning system to generate Spotify’s discover weekly playlist?

To build a machine learning system for Spotify’s Discover Weekly playlist, you would need to analyze user listening habits, preferences, and song metadata. The system could use collaborative filtering, content-based filtering, or a hybrid approach to recommend songs that align with the user’s taste, while also introducing new music that the user might enjoy.

7. How would you build the recommendation algorithm for type-ahead search for Netflix?

To build a type-ahead search recommendation algorithm for Netflix, start with a prefix matching system using a TRIE data structure for efficient look-up. Address dataset bias by focusing on user-typed corpus and incorporating Bayesian updates for true negative values. Enhance recommendations by considering user preferences and clustering user profiles with features like “Coen Brothers Fan.” Implement a multi-layered system for scalability, mapping user profiles to feature sets and caching input strings with condensed profiles.

8. How would you build a model to detect if a post on a marketplace is talking about selling a gun?

To detect firearm sales in marketplace posts, start by clarifying the machine learning question and setting assumptions about the problem scope. Use a combination of text analysis techniques like bag of words or TFIDF to extract features from the text data. Consider using a gradient boosted tree model to handle the imbalance in data, focusing on minimizing false positives and false negatives. Evaluate the model using metrics like the F1 score, and consider augmenting the model with image analysis if necessary.

9. Design a recommendation engine to suggest relevant merchants to users.

Propose a hybrid recommendation system that combines collaborative filtering and content-based methods. Collaborative filtering can use user transaction history to find similar users and recommend merchants they’ve shopped at. Content-based filtering can use merchant category, location, and description to recommend similar merchants to those a user has previously engaged with.

10. How would you build a system to identify potentially fraudulent sellers on the platform?

Focus on features that capture seller behavior over time, such as a high rate of customer disputes, sudden spikes in sales volume, or changes in product categories. A machine learning model can be trained on historical data of known fraudulent sellers to predict which new or existing sellers exhibit similar patterns. The system should include a case management tool for human review of flagged accounts.

11. Design a system to optimize the timing and channel for sending user notifications.

This is an optimization problem that can be tackled with machine learning. For each user and message type, a model could predict the best time of day and the best channel (push, email, SMS) to maximize the probability of engagement. The model would use features like the user’s past interaction times, their time zone, and their stated preferences.

Behavioral or “Culture Fit” Questions

This part of the interview evaluates how well you communicate, collaborate, and reflect on past experiences to demonstrate alignment with PayPal’s values and working style:

12. Give an example of a time you had to explain a complex AI concept to a non-technical stakeholder.

Your answer should demonstrate your ability to communicate clearly and empathetically. Start by describing the stakeholder’s role and what they needed to understand. Explain how you used analogies, visualizations, or simplified language to convey the core message without getting lost in technical jargon, focusing on the business implications.

13. Describe a time you had a disagreement with a team member about a technical approach.

Frame the situation positively, emphasizing a shared goal of finding the best solution for the company. Explain your perspective and the other person’s, detailing how you used data and logical reasoning to discuss the trade-offs. The ideal outcome is a collaborative resolution, whether it was your idea, theirs, or a hybrid solution that was ultimately adopted.

14. How would you answer when an Interviewer asks why you applied to their company?

When responding to why you want to work with a company, focus on aligning your values with the company’s values, projects, and reputation. Highlight your passion for the industry and how the company’s employee benefits can enhance your job performance. Demonstrating a deep understanding of the company’s culture and goals can set you apart from other candidates.

15. How do you stay current with the latest advancements in AI and machine learning?

Demonstrate your passion and proactivity for learning. Mention specific resources you follow, such as key academic conferences (NeurIPS, ICML), ArXiv papers, industry blogs, or influential researchers. It is also good to mention any personal projects or online courses you’ve undertaken to apply new techniques.

16. How would you handle a situation where your model’s predictions were causing a negative customer experience?

Prioritize the customer and demonstrate a sense of urgency. Your first step should be to work with the team to understand the issue’s impact and potentially roll back the model if necessary. Then, describe your analytical process for diagnosing the root cause, whether it’s data drift, a bug in the feature pipeline, or an unforeseen edge case.

17. Tell me about a time a project you were on failed or you made a significant mistake.

Show ownership and a growth mindset by being candid about the failure. Explain the context and what went wrong, focusing on your role in the situation. Most importantly, discuss what you learned from the experience and what specific steps you took or would take in the future to prevent a similar issue.

Preparing for an AI & Machine Learning Engineer role at PayPal requires a strategic approach that aligns with the company’s technical demands and culture. PayPal’s ML Engineers work on sophisticated systems that process billions of transactions and personalize experiences for over 420 million users (higher now in 2025). Success in this role demands mastery of both technical skills and business understanding.

Start by developing proficiency in Python, SQL, and data manipulation techniques essential for PayPal’s technical assessments. Their interview process typically begins with an online assessment featuring LeetCode medium-hard problems, SQL challenges, and ML coding tasks. Focus on algorithms involving graphs, dynamic programming, heaps, and arrays, as these frequently appear in their technical screens.

For system design preparation, study PayPal’s unique challenges around payment processing, fraud detection, and recommendation systems at scale. Practice designing end-to-end ML systems that address real business problems while considering scalability, latency, and cost-efficiency. PayPal particularly values experience with cloud platforms and MLOps for model deployment and monitoring.

Beyond technical skills, prepare for behavioral questions that assess your collaborative abilities and problem-solving approach. Research PayPal’s “agentic commerce” initiatives and AI-driven features to demonstrate alignment with their strategic direction. When interviewing, clearly articulate your thought process and ask clarifying questions to show methodical reasoning.

Conduct mock interviews focusing on the full spectrum of PayPal’s assessment areas: coding challenges, ML fundamentals, system design, and behavioral questions. Practice explaining complex ML concepts via AI Interviewer to approach them simply, and connecting technical solutions to business outcomes. This comprehensive preparation will position you strongly for success in PayPal’s rigorous but rewarding interview process.

What Is the Average Salary for an AI & Machine Learning Engineer Role at PayPal?

Where Can I Read More Discussion Posts on PayPal’s AI & Machine Learning Engineer Role Here in IQ?

You can explore firsthand experiences, interview reviews, and technical tips in our Interview Query community. Visit the discussion section and search for “PayPalAI & Machine Learning Engineer” to find threads shared by past candidates and contributors.

Are There Job Postings for PayPal AI & Machine Learning Engineer Roles on InterviewQuery?

Yes, Interview Query regularly features AI and Machine Learning Engineer job postings for PayPal. You can browse active openings and apply directly through our Jobs section.

Landing a role as an AI & Machine Learning Engineer at PayPal means stepping into a transformative environment where innovation, scale, and business impact intersect every day. From mastering deep technical interviews to showcasing your ability to communicate complex ideas and build production-grade systems, the path to success is both challenging and rewarding.

If you’re ready to dive deeper, start with our AI/ML Engineer Learning Path to structure your preparation. You can also explore our curated System Design Question Collection for PayPal Interviews for coding, ML, and system design practice. And for motivation, read Chris Keating’s success story. All the best!