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Behavioral
a year ago
Could you tell us about why you'd be a good fit for Meta?
Machine Learning Engineer

Meta

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a year ago
Behavioral
a year ago
Can you tell us about a time when you received unpleasant feedback?
Machine Learning EngineerEngineering ManagerUX DesignerUX Researcher

Meta

Sprout Social Logo

Sprout Social

NCR Logo

NCR

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a year ago
Behavioral
a year ago
When have you taken a risk?
Machine Learning EngineerFrontend EngineerAndroid EngineerSales Engineer

Meta

Panasonic Logo

Panasonic

Leidos Logo

Leidos

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a year ago
System Design
a year ago
Develop a taxi-booking service similar to Uber.
Machine Learning Engineer

Meta

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a year ago
Behavioral
a year ago
Describe an occasion when you took ownership
Machine Learning EngineerTechnical Program ManagerSoftware EngineerProduct Manager

Meta

ApplyBoard Logo

ApplyBoard

Naspers Logo

Naspers

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a year ago
System Design
2 years ago
Design a system for real-time processing of network traffic for intrusion detection.
Machine Learning Engineer

Meta

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2 years ago
System Design
2 years ago
Construct a flexible payment planner for Meta.
Machine Learning EngineerSoftware EngineerTechnical Program ManagerSite Reliability Engineering Manager

Meta

GameStop Logo

GameStop

Marqeta Logo

Marqeta

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2 years ago
ML Knowledge
2 years ago
Can you outline what sets DMatrix apart in XGBoost compared to other data structures in similar boosting techniques?
Machine Learning EngineerData Scientist

Meta

Viber Logo

Viber

Deloitte Logo

Deloitte

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2 years ago
System Design
2 years ago
Create a scalable search engine optimized for distributed systems.
Machine Learning Engineer

Meta

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2 years ago
ML Knowledge
2 years ago
For those of us not well-versed in decision trees, could you provide an explanation of bias and variance and how they're pertinent? Additionally, could you share with us which of those two is tougher to deal with: high bias or high variance - and why?
Machine Learning Engineer

Meta

Electronic Arts Logo

Electronic Arts

Cohesity Logo

Cohesity

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2 years ago

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

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