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ML Knowledge
7 months ago
What's your approach to choosing the right number of clusters in K-means, and how do you ensure the optimal balance between extremes?
Machine Learning Engineer

Audible

Canva

trivago

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7 months ago
System Design
9 months ago
How would you architect a scalable face recognition system for a large-scale surveillance network?
Machine Learning Engineer

Audible

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9 months ago
ML Knowledge
10 months ago
What is the significance of LSTM in the development of machine learning and AI systems? How have you utilized this model personally in any of your projects?
Machine Learning Engineer

Audible

Ancestry Logo

Ancestry

Motorola Solutions Logo

Motorola Solutions

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10 months ago
System Design
10 months ago
Develop a distributed message broker service.
Machine Learning Engineer

Audible

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10 months ago
System Design
a year ago
Implement a distributed system for storing key-value pairs.
Machine Learning Engineer

Audible

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a year ago
System Design
a year ago
Design a system for real-time processing of satellite imagery.
Machine Learning Engineer

Audible

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a year ago
ML Knowledge
a year ago
What are the distinguishing characteristics of K-means and Expectation-Maximization (EM) algorithm?
Machine Learning Engineer

Audible

ZocDoc Logo

ZocDoc

Agoda Logo

Agoda

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a year ago
Coding
a year ago
What's your strategy for coding a fair six-sided die roll using a continuous distribution, and how would you test it?
Machine Learning EngineerData Scientist

Audible

Zenefits Logo

Zenefits

VeriSign Logo

VeriSign

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a year ago
ML Knowledge
a year ago
How do you tackle covariate imbalance in machine learning data, and how does it improve model results?
Machine Learning Engineer

Audible

Snowflake Logo

Snowflake

GoCardless Logo

GoCardless

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a year ago
ML Knowledge
a year ago
What is your understanding of the term tf-idf? How does it help in information retrievals?
Machine Learning Engineer

Audible

Redfin Logo

Redfin

Bloomberg Logo

Bloomberg

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a year ago

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*All interview questions are submitted by recent Audible Machine Learning Engineer candidates, labelled and categorized by Prepfully, and then published after being verified by Machine Learning Engineers at Audible.

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