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
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you had to push back on a product manager's request because it would compromise model fairness or introduce bias.
#Ethics
#Communication
#Conflict Resolution
Machine Learning Engineer
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Behavioral
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medium
Describe a situation where you had to debug a sudden drop in a production ML model's performance.
#Debugging
#Production ML
#Ownership
Machine Learning Engineer
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Behavioral
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easy
LinkedIn's core value is 'Members First.' Tell me about a time you prioritized user experience over a short-term metric gain.
#Values
#Decision Making
#User Experience
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you took ownership of a failing project and turned it around.
#Ownership
#Resilience
#Project Management
Machine Learning Engineer
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Behavioral
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easy
Describe a time you had to explain a complex machine learning concept to a non-technical stakeholder.
#Communication
#Mentorship
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you disagreed with a senior engineer on an architectural or modeling decision. How did you resolve it?
#Conflict Resolution
#Collaboration
#Technical Debate
Machine Learning Engineer
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Behavioral
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medium
What is the most challenging machine learning problem you have solved end-to-end, and what was the business impact?
#End-to-End ML
#Impact
#Technical Depth
Machine Learning Engineer
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Coding
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medium
Given the root of a binary tree, collect a tree's nodes as if you were doing this: Collect all the leaf nodes, remove all the leaf nodes, and repeat until the tree is empty.
#Tree
#Depth-First Search
#Binary Tree
Machine Learning Engineer
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Coding
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medium
Design a class which receives a list of words in the constructor, and implements a method that takes two words word1 and word2 and returns the shortest distance between these two words in the list. The method will be called repeatedly.
#Array
#Hash Table
#Two Pointers
#Design
Machine Learning Engineer
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Coding
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easy
You have a long flowerbed in which some of the plots are planted, and some are not. However, flowers cannot be planted in adjacent plots. Given an integer array flowerbed containing 0's and 1's, and an integer n, return true if n new flowers can be planted without violating the no-adjacent-flowers rule.
#Array
#Greedy
Machine Learning Engineer
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Coding
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medium
Given a binary tree, find the lowest common ancestor (LCA) of two given nodes in the tree.
#Tree
#Depth-First Search
#Binary Tree
Machine Learning Engineer
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Coding
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hard
Given an array of points where points[i] = [xi, yi] represents a point on the X-Y plane, return the maximum number of points that lie on the same straight line.
#Array
#Math
#Geometry
#Hash Table
Machine Learning Engineer
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Coding
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easy
Given two strings s and t, determine if they are isomorphic. Two strings s and t are isomorphic if the characters in s can be replaced to get t.
#Hash Table
#String
Machine Learning Engineer
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Coding
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medium
Given an integer array nums, return the number of triplets chosen from the array that can make triangles if we take them as side lengths of a triangle.
#Array
#Two Pointers
#Sorting
#Greedy
Machine Learning Engineer
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Coding
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medium
Given a collection of numbers, nums, that might contain duplicates, return all possible unique permutations in any order.
#Array
#Backtracking
Machine Learning Engineer
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Coding
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hard
Design an algorithm to serialize and deserialize a binary tree. There is no restriction on how your serialization/deserialization algorithm should work.
#String
#Tree
#Depth-First Search
#Breadth-First Search
#Design
Machine Learning Engineer
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Coding
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medium
Given an integer array nums and an integer k, return the kth largest element in the array. Note that it is the kth largest element in the sorted order, not the kth distinct element.
#Array
#Divide and Conquer
#Sorting
#Heap (Priority Queue)
#Quickselect
Machine Learning Engineer
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System Design
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hard
Design LinkedIn's 'People You May Know' (PYMK) recommendation system.
#Recommendation Systems
#Graph Processing
#Scalability
#Machine Learning
Machine Learning Engineer
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System Design
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hard
Design the machine learning architecture for LinkedIn's Feed Ranking.
#Recommendation Systems
#Ranking
#Deep Learning
#System Architecture
Machine Learning Engineer
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System Design
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hard
Design a Job Recommendation system that matches users to newly posted jobs.
#Recommendation Systems
#Information Retrieval
#NLP
#Two-Tower Models
Machine Learning Engineer
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System Design
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hard
Design an Ad Click-Through Rate (CTR) prediction system for LinkedIn Sponsored Content.
#Ads
#CTR Prediction
#Imbalanced Data
#Online Learning
Machine Learning Engineer
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System Design
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medium
Design a system to automatically extract and standardize skills from a user's unstructured profile text and resume.
#NLP
#Named Entity Recognition
#Knowledge Graph
#LLMs
Machine Learning Engineer
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System Design
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medium
Design a personalized search autocomplete system for the LinkedIn search bar.
#Search
#Trie
#Personalization
#Low Latency
Machine Learning Engineer
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System Design
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medium
Design a machine learning system to detect fake profiles and spam accounts at registration.
#Anomaly Detection
#Classification
#Graph ML
#Security
Machine Learning Engineer
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System Design
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hard
Design a notification routing system that decides whether to send a notification via Email, Push, or In-App, and at what time.
#Reinforcement Learning
#Contextual Bandits
#Optimization
#User Experience
Machine Learning Engineer
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Technical
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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
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Technical
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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
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Technical
•
medium
What offline and online evaluation metrics would you use for the LinkedIn Feed ranking model, and why?
#Metrics
#A/B Testing
#Ranking
Machine Learning Engineer
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Technical
•
medium
How do you address position bias in feed ranking or search results?
#Bias Mitigation
#Ranking
#Causal Inference
Machine Learning Engineer
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Technical
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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
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Technical
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medium
How would you design an A/B test to evaluate a new embedding model for job search? How do you handle network effects?
#A/B Testing
#Experimentation
#Network Effects
Machine Learning Engineer
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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
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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
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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
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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
Difficulty Radar
Based on recent AI-sourced data.
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The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.