Top 48
Prep plan
Updated weekly · Last refresh Aug 30

Facebook AI Engineer Interview Questions

The questions to prepare for a Facebook AI Engineer interview. Questions from real interview reports rank first. Updated weekly.

48questions
~7htotal time
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1
PipelinesStart here. 7 questions · ~57 min
Monitor a Production ML PipelineMedium
Recently asked

Design telemetry and monitoring for a production ML pipeline to catch latency, failures, and data quality issues early.

monitoringtelemetryQualityFacebook
Choosing Batch vs Real TimeHard
Recently asked

Evaluate when a pipeline should use stream processing versus scheduled batch based on latency, cost, complexity, and data quality needs.

Stream ProcessingBatch ProcessingDependenciesFacebook
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2
System Design19 questions · ~154 min
Design a Distributed AI Training PlatformHard
Recently asked

Design a distributed AI training platform that supports large-scale data processing, multi-node training, evaluation, and production model rollout.

Feature StoreRetrievalModel ServingFacebook
Design a Real-Time ML Feature StoreHard
Recently asked

Design a shared feature store for training and low-latency inference across many ML systems with strict freshness and consistency needs.

Feature StoreFeature DriftModel ServingFacebook
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3
Coding8 questions · ~65 min
Optimizing Time and Space ComplexityEasy
Recently asked

Explain how to improve coding solutions by reducing time complexity first, then balancing space trade-offs.

Hash TablesArraysGreedyFacebook
Efficient, Readable Data TransformationsEasy
Recently asked

Tests ability to write clear, performant data transformation logic.

Data WranglingefficiencyFacebook
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4
Machine Learning3 questions · ~24 min
Impact of Data Drift on PerformanceMedium
Recently asked

Tests understanding of drift mechanisms and how they translate to measurable degradation.

model performancedata driftModel EvaluationFacebook
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5
Behavioral & Leadership10 questions · ~81 min
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6
More topics1 question · ~8 min
Evaluate Production ReadinessMedium
Recently asked

How to judge whether a model is ready for production using core evaluation metrics and threshold choice.

PrecisionAccuracyRecallFacebook
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