Capital One Machine Learning Engineer Interview Experience — Four Rounds Including a Virtual Credit Card Case Study

There aren't many MLE interview reports for Capital One on the forum, so I'm writing up my own experience to give everyone a reference point. I'll try to be as specific as possible, hoping it helps other people interviewing for MLE later.

There were 4 interviews total, each scheduled for 1 hour, as follows:

1. Set A technical. The interviewer was pretty easygoing, and overall it went smoothly and pleasantly. This part was mainly about some ML fundamentals. For example:

1. bias-variance tradeoff
2. what are parameters and hyperparameters
3. what's the difference between batch inference and real-time inference
4. if the training data volume is very large, how do you handle it?
5. what is feature engineering? why is it important?
6. walk through the ML lifecycle end to end, then talk through one of your own end-to-end projects.

2. Set B technical. This interviewer was also pretty easygoing, chatted pretty pleasantly. The interview had 2 parts.

1. Coding. It was the common "print tree-node paths" problem. Because I had already seen this in another interview report beforehand, I was able to solve it fairly smoothly.
2. This part asked some questions about ML model deployment and monitoring. For example:
   i) how do you deploy your ML model?
   ii) how do you monitor your ML model after deployment?
   iii) say there's a fraud detection model, deployed via an API, but it's missing the <50ms latency requirement — how do you solve this problem?

3. Case interview. This round's interviewer was patient. This round tested the common "virtual credit card" case question. It had 3 parts:

1. Describe the pros and cons of a virtual credit card for the user and for Capital One.
2. After understanding the business requirements, based on various given conditions (card number, transaction ID, transaction type, merchant bound or not, amount, offline/online, Visa/Mastercard, etc.), determine whether a given transaction is valid.
   Here are some of the rules, for example:
   i) Transaction ID: 8-digit number.
   if the 7th digit is 1: valid online, if the 7th digit is 0: valid offline.
   if the 6th digit is 1: transaction type is charge, if the 6th digit is 0: transaction type is authorization.
   ii) Card number: 16-digit number.
   if the 15th digit is 1: merchant bound, if the 15th digit is 0: not merchant bound.
   if the 14th digit is 1: Mastercard, if the 15th digit is 0: Visa.
   if the 13th digit is 1: multi-use, if the 15th digit is 0: one-time use.
   The valid-transaction rules for Visa and Mastercard are also different:
3. Visa: (merchant-bound and multi-use) or (online, amount < $100, not authorization)
4. Mastercard: (amount < $100, online or in-person, not merchant-bound) or (bound, amount > $100)
   Here's an example:
   card #: 1234567891011011
   transaction ID: 50781210
   amount: $150
   online/offline: offline
   transaction type: charge
   merchant bound: yes
   payment: Mastercard
   The answer should be that this transaction is valid.
5. Based on the requirements from part 2, debug the given code, then run the tests. This part isn't hard, but you have to be very careful and check the logic strictly against each requirement to find and fix the bugs.

4. Behavioral. The interviewer was smiling the whole time. There were 3 questions total. While I was answering, the interviewer kept taking notes, and after I finished each answer, he would summarize it back and check with me whether he'd understood it correctly. This round went pretty pleasantly and only took 45 minutes — it ended early. The 3 questions were: 1) how do you challenge the status quo? 2) when you ran into an unexpected difficulty at work, how did you solve it while still delivering on time? 3) how did you achieve something your colleagues viewed as highly difficult?

Overall the interview experience was quite good. The recruiter notified me the next day that I'd passed the interviews, and after that came the team match process.