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MLE · ranking engineersRecSys metrics · ML fundamentals · CV

Meta · Machine Learning Engineer

Ranking and recommendation metrics, collaborative filtering, and computer-vision evaluation — the measured half of Meta's ML loops.

3 phases · 26 problems · 26 unique

Start timed mock5 problems · 4 min each

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Continue track · Accuracy

1Warm-up

0/8

Accuracy
ml-008ML Fundamentals
Easy
Precision, Recall, F1
ml-009ML Fundamentals
Medium
Confusion Matrix Counts
ml-016ML Fundamentals
Easy
Mean Squared Error
ml-017ML Fundamentals
Easy
Cosine Similarity of Feature Vectors
ml-046ML Fundamentals
Easy
Precision at K
ml-222ML Fundamentals
Easy
Mean Average Precision
ml-224ML Fundamentals
Medium
Mean Reciprocal Rank
ml-225ML Fundamentals
Easy

2Core

0/10

Matthews Correlation Coefficient
ml-069ML Fundamentals
Medium
Cohen's Kappa
ml-070ML Fundamentals
Medium
AUC by Trapezoid
ml-074ML Fundamentals
Medium
Lift at K
ml-102ML Fundamentals
Medium
Population Stability Index
ml-174ML Fundamentals
Medium
Lift Confidence Interval
ml-185ML Fundamentals
Medium
Sample Size per Variant
ml-186ML Fundamentals
Medium
Item-Based Cosine Predict
ml-219ML Fundamentals
Medium
CLIP Similarity Matrix
cv-249Computer Vision
Medium
Contrastive Logits
cv-250Computer Vision
Medium

3Hard

0/8

Expected Calibration Error by Probability Bins
ml-101ML Fundamentals
Hard
Normalized Mutual Information
ml-151ML Fundamentals
Hard
User-Based CF Predict
ml-218ML Fundamentals
Hard
Matrix Factorization SGD Step
ml-220ML Fundamentals
Hard
NDCG at K
ml-223ML Fundamentals
Hard
PR AUC Trapezoid
ml-243ML Fundamentals
Hard
Non-Max Suppression
cv-040Computer Vision
Hard
Mean IoU over Classes
cv-168Computer Vision
Hard