Machine Learning Engineer · Onsite · Multiple Types

Netflix — Machine Learning Engineer ✅ Passed

Level: Senior-Level

Round: Onsite · Type: Multiple Types · Difficulty: 3/10 · Duration: 165 min · Interviewer: Unfriendly

Topics: System Design, Behavioral, Data Science, Machine Learning, Algorithms

Location: Los Angeles, CA, US

Interview date: 2026-01-15

Summary

Interview Rounds Overview

• Round 1: Coding
• Round 2: Partnership / Technical Discussion
• Round 3: System Design

Details

Round 1: Coding (60 minutes)

I was asked to build a service that selects a movie or show title to display on a user's homepage billboard. Each title has a score representing its relevance to the user. The service should return titles in descending order of score, but with a rotation rule to avoid showing the same title repeatedly.

APIs to support:

• upsertTitleScore(title_id: str, score: float): Adds a new title or updates the score of an existing title.
• getTopTitle() -> str: Returns the title that should currently be displayed on the homepage.

Requirements:

• Titles with higher scores should be returned preferentially.
• The service should avoid returning the same title twice in a row, if other titles are available.
• A title can be returned again in subsequent calls after it has been returned once.
• If there is only one title in the system, it can be returned repeatedly.
• upsertTitleScore may be called multiple times with the same title_id, and the score needs to be updated correctly.

Round 2: Partnership (45 minutes)

This round involved a discussion with a data scientist, covering both technical and behavioral aspects. The focus was on how I would handle customer requests and understand customer pain points. Examples included:

• How would I help improve the model iteration process if it was too slow?
• What would I do if a model was deployed but did not perform as expected?
• How could I help them improve the data access process if they couldn't get the data to train the model (due to ACL or similar issues)?

The interviewer seemed to be looking for open-ended suggestions and an understanding of the challenges faced in modeling. I made sure to acknowledge the imperfections in practice and anticipate challenges to my suggestions.

Round 3: System Design (60 minutes)

I had to design a system to allocate computing resources (CPU, GPU, and multiple clusters) for machine learning jobs. The system should run user-submitted jobs and efficiently utilize the available computing resources.