Professional networking platform with rich data and ML-driven recommendations.
4 Rounds
~21 Days
Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Data Scientist
•
Technical
•
medium
What features and models would you use to detect fake profiles or spam accounts on LinkedIn?
#Classification
#Anomaly Detection
#Feature Engineering
Data Scientist
•
Technical
•
medium
How would you build a model to predict which users are likely to churn from their LinkedIn Premium subscription?
#Churn Prediction
#Survival Analysis
#Classification
Data Scientist
•
Technical
•
hard
Explain how you would extract and normalize skills from a raw text resume.
#NLP
#Named Entity Recognition
#Embeddings
Machine Learning Engineer
•
Technical
•
medium
How do you address position bias in feed ranking or search results?
#Bias Mitigation
#Ranking
#Causal Inference
Machine Learning Engineer
•
Technical
•
hard
Explain the difference between point-wise, pair-wise, and list-wise learning to rank. Which approach fits LinkedIn search best?
#Learning to Rank
#Search
#Algorithms
Machine Learning Engineer
•
Technical
•
medium
Describe how you would handle highly imbalanced data when predicting ad clicks (where CTR is typically < 1%).
#Imbalanced Data
#Sampling
#Loss Functions
Machine Learning Engineer
•
Technical
•
medium
How would you handle the cold-start problem for newly posted jobs in the Job Recommendation system?
#Cold Start
#Content-Based Filtering
#Exploration vs Exploitation
Machine Learning Engineer
•
Technical
•
medium
What is the role of calibration in CTR prediction, and how do you calibrate a model?
#Model Calibration
#Ads
#Probability
Machine Learning Engineer
•
Technical
•
hard
How would you leverage Large Language Models (LLMs) to improve LinkedIn's messaging 'Smart Reply' suggestions while maintaining low latency?
#LLMs
#NLP
#Generative AI
#Latency
Machine Learning Engineer
•
Technical
•
hard
How do you ensure real-time model serving latency remains under 50ms for a complex deep learning feed ranking model?
#Model Serving
#Latency
#Optimization
#MLOps
Machine Learning Engineer
•
Technical
•
hard
Explain how you would use Graph Neural Networks (GNNs) to generate user embeddings from LinkedIn's connection graph.
#Graph Neural Networks
#Embeddings
#Deep Learning
Machine Learning Engineer
•
Technical
•
medium
What offline and online evaluation metrics would you use for the LinkedIn Feed ranking model, and why?
#Metrics
#A/B Testing
#Ranking
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
SimulateUnwritten Rules
Think Out Loud
Always explain your thought process before writing code or drawing architecture.