Machine Learning Engineer · Phone Screen · Coding

Pinterest — Machine Learning Engineer ❌ Failed

Level: Mid-Level

Round: Phone Screen · Type: Coding · Difficulty: 7/10 · Duration: 60 min · Interviewer: Unfriendly

Topics: Machine Learning, Overfitting, Underfitting, Regularization, Sampling

Location: San Francisco Bay Area

Interview date: 2026-01-20

Got offer: False

Summary

During the one-hour phone screen, the first half was focused on machine learning fundamentals, including overfitting, underfitting, and regularization (L1, L2 loss). The coding portion involved a custom problem where I had to sample a string from a list of strings (similar to online reviews) based on their corresponding scores (floating-point numbers ranging from negative infinity to positive infinity).

Details

The coding question I received was:

Given a list of strings (like online reviews) and a corresponding score for each string (floating-point numbers from negative infinity to positive infinity), implement a sampling method to select a string based on the score distribution.

My Approach:

1. I first analyzed the distribution of the scores.
2. Then, I implemented a sampling algorithm to select a string based on its score.

Key Insights:

• Handling the extreme range of scores (negative infinity to positive infinity) required careful consideration to avoid numerical instability.
• The choice of sampling algorithm significantly impacted the efficiency and accuracy of the string selection.