How to Crack AI/ML Interviews in 2025: Complete Preparation Guide (FAANG to Startups)
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The Interview That Changed Everything
Meet Raj, a 27-year-old software engineer earning ₹8 LPA.
- October 2024: Started learning AI/ML
- April 2025: Applied to 50 companies
- May 2025: Got 3 offers:
- • Amazon: ₹45 LPA
- • Early-stage startup: ₹28 LPA + equity
- • Indian unicorn: ₹38 LPA
He chose Amazon.
His monthly salary went from ₹66,000 to ₹3,75,000.
The difference? He didn't just learn AI/ML. He learned how to interview.
This guide reveals exactly what companies ask, how to prepare, and how to negotiate like Raj did.
Before You Start: Master the Fundamentals
Make sure you have the core skills companies are looking for. Read our comprehensive guides:
Understanding the AI/ML Interview Process
Standard Interview Flow (Most Companies)
Round 1: Phone Screen (30-45 min)
- • Basic ML concepts
- • Python coding
- • Past projects discussion
Round 2: Technical Round 1 (60-90 min)
- • ML algorithms deep dive
- • Coding (LeetCode medium)
- • Model evaluation
Round 3: Technical Round 2 (60-90 min)
- • System design for ML
- • Project deep dive
- • Advanced concepts
Round 4: ML Case Study (60 min)
- • Real business problem
- • Design ML solution
- • Trade-offs discussion
Round 5: Behavioral (30-45 min)
- • Leadership principles
- • Past experiences
- • Culture fit
Round 6: Hiring Manager (30 min)
- • Team fit
- • Salary discussion
- • Career goals
Interview Duration Timeline
From application to offer:
- Startups: 2-4 weeks
- Unicorns: 4-6 weeks
- FAANG: 6-10 weeks
The 4 Types of AI/ML Interview Questions
Type 1: ML Theory & Concepts (40% weightage)
What they test: Deep understanding of algorithms
Q1: Explain how gradient descent works. Why might it fail to converge?
Strong Answer:
"Gradient descent iteratively updates parameters by moving in the direction of steepest descent (negative gradient) of the loss function.
θ_new = θ_old - α * ∇L
It might fail to converge due to:
- • Learning rate too high - Overshoots minimum, bounces around
- • Learning rate too low - Takes forever, gets stuck in plateaus
- • Local minima - Gets trapped (less issue with neural networks)
- • Saddle points - Gradient is zero but not a minimum
- • Vanishing gradients - In deep networks, gradients become too small
Solutions: Adaptive learning rates (Adam), momentum, batch normalization, proper initialization."
Q2: Explain bias-variance tradeoff. How do you detect high bias vs high variance?
Strong Answer:
"Bias: Error from wrong assumptions (underfitting)
Variance: Error from sensitivity to training data (overfitting)
Detection:
- • High bias: Low training accuracy, low test accuracy (both bad)
- • High variance: High training accuracy, low test accuracy (huge gap)
Solutions:
High bias: More complex model, more features, less regularization
High variance: More data, regularization (L1/L2, dropout), simpler model
Sweet spot: Balanced error on both training and test sets."
Type 2: Coding (30% weightage)
What they test: Python proficiency + algorithmic thinking
# Pattern 1: Data Manipulation
import pandas as pd
import numpy as np
def clean_data(df):
# Identify columns to drop (>30% missing)
threshold = len(df) * 0.3
cols_to_drop = [col for col in df.columns
if df[col].isna().sum() > threshold]
df = df.drop(columns=cols_to_drop)
# Fill numerical columns
numerical_cols = df.select_dtypes(include=[np.number]).columns
for col in numerical_cols:
if df[col].isna().any():
df[col].fillna(df[col].median(), inplace=True)
# Fill categorical columns
categorical_cols = df.select_dtypes(include=['object']).columns
for col in categorical_cols:
if df[col].isna().any():
df[col].fillna(df[col].mode()[0], inplace=True)
return dfLeetCode Problems for AI/ML:
- • Two Sum, Three Sum (arrays)
- • Valid Parentheses (stacks)
- • Binary Tree traversals (trees)
- • Dynamic Programming basics
- • Sliding Window problems
Practice: 150 LeetCode problems (Easy: 50, Medium: 90, Hard: 10)
Type 3: System Design for ML (20% weightage)
What they test: Production thinking + scalability
Q1: Design a YouTube recommendation system
Strong Answer Structure:
1. Clarify Requirements
- • Scale: 2 billion users, 500 hours video/minute uploaded
- • Latency: <200ms for recommendations
- • Personalization: Yes, based on watch history
- • Cold start: Handle new users/videos
2. High-Level Architecture
↓
[Feature Store]
↓
[Candidate Generation] → [Ranking Model] → [Re-ranking]
3. Model Design
- • Stage 1: Candidate Generation - Two-tower model, ANN for fast retrieval, generate top 1000
- • Stage 2: Ranking - Deep neural network, predict P(watch | user, video, context), rank top 100
- • Stage 3: Re-ranking - Diversity filter, freshness boost, quality filter, final top 20
Type 4: ML Case Studies (10% weightage)
What they test: Business thinking + ML application
Q: Our e-commerce app has 30% cart abandonment. Design an ML solution.
1. Problem Framing
Goal: Reduce abandonment from 30% to 20%
2. ML Approach
Model: Binary classification (will_checkout?)
Model Choice: Gradient Boosting (XGBoost)
3. Intervention Design
- • High Risk (P < 0.3): Send push notification with 10% off
- • Medium Risk (0.3-0.7): Browser notification
- • Low Risk (P > 0.7): No intervention
Company-Specific Interview Patterns
FAANG (Google, Amazon, Meta, Apple, Netflix)
Focus Areas:
- • System design (40%)
- • Coding (30%)
- • ML theory (20%)
- • Behavioral (10%)
Difficulty: Very High
Bar: Top 1-5% candidates
Preparation Time: 6-9 months
Indian Unicorns (Flipkart, Swiggy, Razorpay)
Focus Areas:
- • ML theory (35%)
- • Coding (25%)
- • System design (25%)
- • Projects (15%)
Difficulty: High
Bar: Top 10-15% candidates
Preparation Time: 4-6 months
AI Startups (OpenAI, Anthropic, local AI startups)
Focus Areas:
- • LLM knowledge (40%)
- • Coding (30%)
- • Projects (20%)
- • Research understanding (10%)
Difficulty: Very High (different skills)
Bar: LLM expertise + can ship code fast
Preparation Time: 3-6 months (if you know LLMs)
Service Companies (TCS Digital, Infosys, Wipro)
Focus Areas:
- • ML basics (40%)
- • Python (30%)
- • Projects (20%)
- • Communication (10%)
Difficulty: Medium
Bar: Top 30-40% candidates
Preparation Time: 3-4 months
The Ultimate Preparation Strategy
Timeline: 4-Month Intensive Prep
Month 1: ML Theory Mastery
Week 1-2: Supervised Learning
- • Linear/Logistic Regression
- • Decision Trees, Random Forest
- • SVM, KNN, Ensemble methods
Week 3-4: Unsupervised Learning
- • Clustering (K-means, DBSCAN)
- • PCA, t-SNE
- • Anomaly detection
Resources: Andrew Ng's ML course, "Hands-On Machine Learning" book, StatQuest (YouTube)
Month 2: Deep Learning & Projects
Week 1-2: Neural Networks
- • Backpropagation
- • CNN architectures
- • RNN/LSTM
Week 3-4: Build Projects
- • Image classifier
- • Sentiment analyzer
- • Time series predictor
Resources: Fast.ai course, CS231n (Stanford)
Month 3: Coding + System Design
Week 1-2: LeetCode
- • 50 problems (Easy: 20, Medium: 25, Hard: 5)
- • Focus on arrays, trees, DP
Week 3-4: System Design
- • Read "Designing ML Systems" (Chip Huyen)
- • Practice 5 design questions
- • Draw architectures
Month 4: Company Research + Mock Interviews
Week 1-2: Company Preparation
- • Research target companies
- • Understand their ML use cases
- • Tailor resume for each
Week 3-4: Mock Interviews
- • 10 mock interviews (Pramp, Interviewing.io)
- • Get feedback
- • Improve weak areas
Daily Schedule (Part-Time: 3-4 hours/day)
Weekdays:
- • 1 hour: ML theory (watch lecture, read book)
- • 1 hour: Coding practice (LeetCode)
- • 1 hour: Hands-on project
- • 30 min: Review & notes
Weekends:
- • 2 hours: System design practice
- • 2 hours: Build projects
- • 1 hour: Mock interviews
Weekly Goals
- ✓ Complete 10 LeetCode problems
- ✓ Finish 1 ML topic thoroughly
- ✓ Make progress on 1 project
- ✓ 1 system design practice
- ✓ 1 mock interview (after month 3)
The Behavioral Round (Often Overlooked)
STAR Method for Answering
- Situation: Set context
- Task: Your responsibility
- Action: What you did
- Result: Outcome (quantify!)
Example Question: "Tell me about a time you improved model performance"
❌ Bad Answer:
"I tried different algorithms and got better accuracy."
✅ Good Answer:
Situation: Our fraud detection model had 85% recall but 30% false positive rate, causing customer complaints.
Task: I was tasked to reduce false positives while maintaining recall.
Action:
- • Analyzed false positives - found 60% were small transactions from new users
- • Added features: user age, average transaction size, location consistency
- • Implemented ensemble (XGBoost + LightGBM) with probability calibration
- • Created threshold optimization using business cost function
Result:
- • Reduced false positives from 30% to 12%
- • Maintained 85% recall
- • Saved company $500K annually in customer service costs
- • Model now in production serving 10M daily transactions
Common Behavioral Questions
- • "Why do you want to work here?"
- • "Tell me about a challenging project"
- • "How do you handle disagreement with your team?"
- • "Tell me about a time you failed"
- • "Where do you see yourself in 5 years?"
Preparation: Prepare 5-7 stories using STAR method
Salary Negotiation Strategies
The Numbers (India, 2025)
Entry-Level (0-2 years):
- • Startups: ₹8-15 LPA
- • Unicorns: ₹12-20 LPA
- • FAANG: ₹25-35 LPA
Mid-Level (2-5 years):
- • Startups: ₹15-30 LPA
- • Unicorns: ₹25-45 LPA
- • FAANG: ₹40-65 LPA
Senior (5+ years):
- • Startups: ₹30-60 LPA
- • Unicorns: ₹45-80 LPA
- • FAANG: ₹65-120 LPA
Negotiation Tactics
1. Never Give Number First
❌ Bad:
Recruiter: "What's your expected salary?"
You: "₹25 LPA"
✅ Good:
Recruiter: "What's your expected salary?"
You: "I'm more focused on the role and team fit. What's the range for this position?"
2. Use Competing Offers
"I have another offer at ₹40 LPA, but I'm more excited about this role. Can you match or come close?"
3. Negotiate Beyond Base Salary
Components:
- • Base salary (60-70%)
- • Bonus (10-15%)
- • Stock/ESOP (15-25%)
- • Joining bonus (one-time)
- • Relocation (if applicable)
Example:
Company offers: ₹30 LPA base
You: "Can we do ₹32 LPA base + ₹3L joining bonus + ₹5L ESOP?"
Real Example: Negotiation That Worked
Initial Offer: ₹35 LPA (₹30L base + ₹5L bonus)
Counter: "Thank you for the offer. I'm excited about the role. Based on my research and competing offers, I was expecting closer to ₹42 LPA. Can we discuss?"
Company Response: ₹38 LPA (₹32L base + ₹4L bonus + ₹2L stocks)
Final Counter: "Can we add a ₹2L joining bonus to help with transition costs?"
Final Offer: ₹40 LPA (₹32L base + ₹4L bonus + ₹2L stocks + ₹2L joining)
Increase: ₹5L (14% more than initial)
Common Mistakes to Avoid
❌ Mistake 1: Over-preparing Theory, Under-preparing Coding
Problem: Can explain algorithms but can't code them
Solution: 60% coding practice, 40% theory
❌ Mistake 2: Not Practicing System Design
Problem: Fail senior rounds
Solution: Practice 10+ system design questions
❌ Mistake 3: Weak Communication
Problem: Know answer but can't explain
Solution: Practice explaining to non-technical friends
❌ Mistake 4: Ignoring Behavioral Round
Problem: Pass technical but fail behavioral
Solution: Prepare 5-7 STAR stories
❌ Mistake 5: Applying Without Preparation
Problem: Burn opportunities at dream companies
Solution: Prepare 3-4 months, then apply
Your Interview Preparation Checklist
3 Months Before
- ☐ Start LeetCode (10 problems/week)
- ☐ Review ML theory (1 topic/week)
- ☐ Build 2 projects
- ☐ Read system design book
2 Months Before
- ☐ Complete 80 LeetCode problems
- ☐ Master all ML algorithms
- ☐ Practice 5 system design questions
- ☐ Polish resume
1 Month Before
- ☐ Start applying
- ☐ Do 5 mock interviews
- ☐ Prepare behavioral stories
- ☐ Research target companies
Interview Week
- ☐ Review common questions
- ☐ Rest properly
- ☐ Prepare questions for interviewer
- ☐ Be confident!
Success Stories
Story 1: From ₹10 LPA to ₹45 LPA (Amazon)
- Background: 3 years software engineer
- Preparation: 5 months (3-4 hours daily)
- Strategy: Focused on system design + LeetCode
- Result: ₹45 LPA offer from Amazon
- Key Takeaway: System design practice made the difference
Story 2: Fresh Graduate → ₹28 LPA (AI Startup)
- Background: B.Tech graduate
- Preparation: 8 months (full-time learning)
- Strategy: Built 10 LLM projects, active on GitHub
- Result: ₹28 LPA at AI startup
- Key Takeaway: Strong portfolio > degree
Story 3: Non-CS Background → ₹22 LPA (Unicorn)
- Background: Mechanical engineer, self-taught
- Preparation: 12 months (part-time)
- Strategy: Bootcamp + 15 projects + 200 LeetCode
- Result: ₹22 LPA at Indian unicorn
- Key Takeaway: Consistency beats talent
Conclusion: Your Interview Success Formula
The Math:
- • 4 months preparation
- • 3-4 hours daily practice
- • 150 LeetCode problems
- • 10 system design practices
- • 5 mock interviews
- • 3-5 polished projects
- = ₹25-60 LPA job
The Reality:
- • 80% of candidates don't prepare properly
- • 90% give up before 100 applications
- • 95% don't negotiate salary
- Be the 5% who does.
Start preparing today. In 4 months, you could be fielding multiple offers.
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Tags: #AIInterviews #MLInterviews #InterviewPrep #FAANG #TechInterviews #SalaryNegotiation #CareerGrowth #AIJobs #CodingInterview #SystemDesign #MachineLearning
Last Updated: December 2025 | Good luck with your interviews!