Machine Learning GenAI Interview Preparation Guide

Contents

• 📋 Strategic Overview
  • Optimization Strategy
• 💻 Part 1: Coding
  • Data Structure and Algorithms Problems
    • Concepts Review
    • Coding Questions List
  • Machine Learning Coding
• 🧠 Part 2: Machine Learning Design
• 📚 Part 3: ML/Deep Learning/genAI Concepts
  • Machine Learning
  • Data Science
  • LLM/genAI
    • Transformer
  • AI Agent
  • Awesome GenAI Review
  • Fast Paper Summary Tool
• 📝 Resume Preparation using AI
• 🎬 Mock Interview
• 🗓️ 3-Month Interview Preparation Timeline
  • Month 1: Foundations
  • Month 2: Building & Applying
  • Month 3: Specialization & Interviews
  • Key Milestones
  • Behavioral Interview Preparation

📋 Strategic Overview

This comprehensive THREE month preparation framework is designed for Machine Learning Engineers, ML Scientists, and Applied/Data Scientists aiming to excel in technical interviews. The structured approach addresses four critical assessment areas: Algorithmic Coding, Machine Learning System Design, Technical ML/AI Concepts, and Professional Behavioral Assessment.

Optimization Strategy

This evidence-based preparation timeline delivers:

• 50%+ Efficiency Improvement through targeted, high-impact learning pathways
• Strategic Topic Prioritization eliminating low-yield subject areas
• Progressive Skill Development from foundational to specialized expertise
• Application Timing Optimization aligned with your preparation lifecycle
• Continuous Improvement Methodology leveraging early interview experiences

💻 Part 1: Coding

Data Structure and Algorithms Problems

Concepts Review

• AlgoMonster - Pattern-based algorithm interview preparation

  • Keyword to Algorithm Guide: Learn to map problem descriptions to appropriate solution techniques
  • Algorithm Templates: Reusable code frameworks for common patterns like binary search, BFS/DFS, and dynamic programming

• HelloInterview - Visual algorithm learning platform

  • Interactive Visualizations: Animated walkthroughs showing algorithm execution in real-time
  • Curated Key Questions: Essential interview problems tagged by company and difficulty level
  • Features comprehensive step-by-step explanations with visual aids for complex algorithms
  • Covers core patterns like tree traversals, dynamic programming, and graph algorithms with visual clarity
  • Includes solution approaches and common techniques used at top tech companies

Coding Questions List

• NeetCode 150 - Curated list of essential LeetCode problems
  • Blind 75: Core problems for essential patterns
  • NeetCode 150: Comprehensive coverage for most interviews
  • NeetCode 250: Advanced preparation for top tech companies
  • Features excellent video explanations demonstrating how to verbalize your thought process during interviews (particularly valuable for Meta-style interviews)
  • Problems organized by pattern with detailed solution walkthrough videos
  • Teaches interview communication skills alongside technical solutions
• LeetCode Company Tags - Filter problems by company
• Bugfree.ai - Platform specializing in debugging LeetCode solutions and summarizing optimal approaches by question type, helping users understand algorithmic patterns and common pitfalls while providing concise explanations of efficient solutions for technical interviews
• Flashcard - Review Leetcode anywhere and any time
• Data manipulation (Optional)
  • Pandas/SQL

Machine Learning Coding

• DeepML - Interactive platform for learning and practicing machine learning concepts from scratch
  • Features a LeetCode-style interface for immediate feedback and testing
  • Problems organized by difficulty level and concept categories
• ML-From-Scratch - Comprehensive collection of machine learning algorithms implemented from scratch in Python
• Practice ML Collection - Extensive compilation of ML implementations and code examples for practical applications
  • Pros: Excellent resource for practical ML implementation in startup environments
  • Cons: Lacks clear categorization, requiring specific keyword searches to find relevant content such as pytorch, EDA, machine learning, data cleaning, neural network, outlier detection, etc.

🧠 Part 2: Machine Learning Design

• Machine Learning System Design Interview - Alex Xu, Zhe Li, Dinghan Shen (ByteByteGo, 2021) - Comprehensive guide for ML system design interviews covering essential concepts and frameworks for designing scalable ML systems in production. Amazon
  • Read Through all reference links after every session.
• ML Design Mock - Get feedback from real FANNG comapnies.
• 500 ML Design Cases by Evidently
  • Real-world ML and LLM systems

📚 Part 3: ML/Deep Learning/genAI Concepts

Machine Learning

• Machine Learning Glossary
• ML Interview Questions with Code
• MLE Flashcard

Data science

• Probability Cheatsheets
• Data Science Cheatsheets
• Ace the Data Science Interview Nick Singh & Kevin Huo (2021) - Comprehensive guide covering SQL, statistics, probability, ML, and product metrics with 201 interview questions and solutions to help data scientists prepare effectively for technical and behavioral interviews. Website
• A/B Testing
  • A Summary of Udacity A/B Testing Course
  • Experimentation Paper
• Causal Inference
  • Causal Inference Paper
  • Causal Inference 101 Video

LLM/genAI

• LLM Interview Questions
• Hands-On Large Language Models
• AI Flashcard

Transformer

• Transformer Math
• Transformer from Scrach
• Transformers Explained Visually
  • Part1
  • Part2
  • Part3

AI Agent

• Prompt Engineering Interview Questions
• LLM RAG Interview Questions
• Awesome genAI guide

Awesome GenAI Review, If you have more time ...

• AI/ML 30 Day Playbook
• GenAI 5 Week by Hand

Fast Paper Summary Tool

• NotebookLM - Google's AI-powered notebook that summarizes and organizes research papers
• Zotero + AI plugins - Reference manager with AI plugins for summarizing and annotating papers

📝 Resume Preparation using AI

• Resume Editing
  • Highlight keywords matching with job descriptions.

🎬 Mock Interview

• HelloInterview
• ML Design Mock

🗓️ 3-Month Interview Preparation Timeline

Month 1: Foundations

Week	Focus Area	Tasks	Status
1-2	Data Structures & Algorithms	• Review arrays & strings • Review linked lists • Review stacks & queues • Start NeetCode 150: Two Sum, Valid Parentheses, Merge Two Sorted Lists	📅 Scheduled
1-2	ML Coding	• Basic NumPy & Pandas exercises • Implement data preprocessing functions • Practice ML algorithms implementation	📅 Scheduled
3-4	Data Structures & Algorithms	• Review trees & graphs: Maximum Depth of Binary Tree • Review dynamic programming: Climbing Stairs • Continue NeetCode 150: Coin Change	📅 Scheduled
3-4	ML Coding	• Implement models from scratch (linear regression, decision trees) • Feature engineering practice • Cross-validation implementation	📅 Scheduled

Month 2: Building & Applying

Week	Focus Area	Tasks	Status
5-6	LeetCode Practice	• Continue NeetCode 150: Course Schedule, Longest Substring Without Repeating Characters • Start company-specific problems: Google Tagged, Meta Tagged • Focus on medium difficulty problems	📅 Scheduled
5-6	Job Applications	• Resume optimization & ATS testing • LinkedIn/GitHub portfolio updates • Start applying to mid-tier companies	📅 Scheduled
5-6	ML Concepts	• Review ML fundamentals • Study ML design patterns • Begin ML system design practice	📅 Scheduled
7-8	LeetCode Practice	• Complete advanced NeetCode problems: Word Break, Meeting Rooms II • Continue company-specific LeetCode: Amazon Tagged • Mock coding interviews	📅 Scheduled
7-8	Job Applications	• Research dream companies • Apply to dream companies • Network with employees at target companies	📅 Scheduled
7-8	ML & GenAI	• Study Transformers & LLM concepts • Review GenAI interview questions • Practice ML case studies	📅 Scheduled

Month 3: Specialization & Interviews

Week	Focus Area	Tasks	Status
9-10	Mock Interviews	• HelloInterview ML system design practice • MeetaPro ML design mocks • Behavioral interview practice with experienced mentor	📅 Scheduled
9-10	Interview Practice	• Company-specific research (products, tech stack) • Initial interviews with mid-tier companies • Refine behavioral STAR stories	📅 Scheduled
9-10	ML Concepts	• Deep learning fundamentals • Production ML systems • Evaluation metrics & model deployment	📅 Scheduled
11-12	Advanced Interviews	• Interviews with dream companies • Final company-specific preparation • Interview retrospectives & adjustments	📅 Scheduled
11-12	Mock Interviews	• Company-specific practice sessions • End-to-end interview simulation (coding + ML design + behavioral) • Focus on weak areas from prior interviews	📅 Scheduled
11-12	ML Specialization	• Company-specific technologies • ML ethics and responsible AI • Final review of flashcards and key concepts	📅 Scheduled

Key Milestones

Milestone	Target Date	Status
Complete DS&A Review	End of Week 4	📅 Scheduled
Finish NeetCode 150	End of Week 8	📅 Scheduled
Resume & Portfolio Ready	End of Week 6	📅 Scheduled
10 Mock Interviews Completed	End of Week 10	📅 Scheduled
5 Behavioral Mock Interviews	End of Week 8	📅 Scheduled
STAR Stories Prepared	End of Week 7	📅 Scheduled
First Applications Sent	Mid-Week 6	📅 Scheduled
Dream Company Applications	End of Week 8	📅 Scheduled
First Round Interviews	Weeks 9-10	📅 Scheduled
Dream Company Interviews	Weeks 11-12	📅 Scheduled

Behavioral Interview Preparation

Category	STAR Stories to Prepare	Example Questions
Leadership & Initiative	• Project turnaround • Team motivation • Process improvement	• Tell me about a time you led a project • Describe a situation where you influenced without authority • How have you improved a process?
Problem Solving	• Technical debugging • Resource constraints • Ambiguous requirements	• Describe a difficult technical problem you solved • Tell me about working with incomplete information • How did you handle a project with tight deadlines?
Teamwork & Collaboration	• Cross-functional project • Difficult team member • Remote collaboration	• How do you work with non-technical stakeholders? • Tell me about resolving a conflict with a teammate • Describe a successful collaboration
Failure & Resilience	• Failed project • Missed deadline • Learning from mistakes	• Tell me about a time you failed • How do you handle criticism? • Describe overcoming a significant setback
ML-Specific	• Model underperformance • Ethical ML challenge • ML research to production	• How did you improve a failing model? • Tell me about considering fairness in ML • Describe deploying research to production

Legend:

• 📅 Scheduled
• ⏳ In Progress
• ✅ Completed