I applied online. I interviewed at Pinterest in Oct 2023
Interview
A phone call with the recruiter, followed by a video call for a technical interview. I have to say I didn't go very prepared and the interview was kinda difficult. The coding problem was not straightforward and you couldn't have done it in time if you had not practiced similar problems.
Interview questions [1]
Question 1
- Describe a past project - What is regularization? What are different types of it? - If you have a million data points, would you use DNN or KNN? What's the difference in inference time? - Coding problem was writing the Splitwise algorithm. Given a list of payers and payees, find the best way to settle expenses.
The phone screen was a mix of behavioral questions and some basic technical concepts, which was a bit different from what I anticipated. After that, I faced a technical round that focused on implementing sparse matrix operations. To my surprise, the coding question was nearly identical to what I'd practiced in the algorithm section on PracHub just days before. The onsite interview included some more DSA challenges and a discussion on machine learning concepts, which helped me feel well-prepared. Overall, the experience was smooth, and I accepted the offer afterward.
Interview questions [1]
Question 1
Implement sparse matrix storage, addition, and multiplication
The assessment time was very short and harsh. There were lots of questions with a very limited time. I liked the other big companies' assessments better. For example, Meta and Google assessments are through talking to a person while coding.
I applied for the new grad MLE position. The process included an online assessment, a 20-minute HR call, and four onsite interviews (two coding and two machine learning system design).