How a Machine Learning Engineer Scored $750K+ Offers from Snap, Google, and Apple — Study Guide &…

How a Machine Learning Engineer Scored $750K+ Offers from Snap, Google, and Apple — Study Guide & Resources

A complete guide to crack ML Engineer interview with resources

Hello guys recently I come across an interesting reddit post about an ML Engineering securing $750K offers from Snap, Google, and Apple. This offer amount was large enough to catch my attention and I read through his long message (shown below).

The message is full of gems and resources and it does warranted an article so I created one for you. If you also aspire to become an ML Engineer or want to know how to crack those kind of salaries then this post is for you.

But first,

Here is the full reddit thread:

How a Machine Learning Engineer Scored $750K+ Offers from Snap, Google, and Apple?

Landing a $750,000+ offer from top tech companies like Snap, Google, and Apple is no small feat — but this Machine Learning Engineer shared a transparent and actionable summary of the exact resources and strategies that helped them get there.

Whether you’re preparing for ML interviews at FAANG or building your way toward high-paying technical roles, this guide has golden nuggets of advice — and we’ve also added top-rated courses and books to help you study smarter.

1. Coding Interview Prep

📘 Resources Used:

CLRS (Introduction to Algorithms)
The Algorithm Design Manual by Steven Skiena
Cracking the Coding Interview by Gayle Laakmann McDowell
Elements of Programming Interviews in Python
LeetCode (used extensively)

🔗 Recommended Courses:

AlgoMonster— Excellent for curated problems with video explanations
Grokking Coding Interview Patterns (DesignGurus)
Data Structures and Algorithms Bootcamp (Udemy)

2. ML Fundamentals and ML System Design

This section was key to their success. They dived deep into modern ML and AI system concepts.

📚 Resources:

Dive Into Deep Learning (free online book)
Machine Learning Interviews by Aminian and Xu
Generative AI System Design Interviews by Aminian and Sheng
Super Study Guide and Cheat Sheets by Amidi brothers
AI Engineering: Building Apps with Foundation Models by Chip Nguyen
Designing Machine Learning Systems by Nguyen

🔗 Recommended Courses:

Machine Learning Specialization by Andrew Ng (Coursera)
AI Engineering with Foundation Models (DeepLearning.AI)
ML System Design for Interviews by Educative
Grokking the Generative AI System Design— Educative.io

3. System Design (Mostly ML Focused)

Though they rarely faced traditional system design rounds, ML-specific design skills were essential.

📘 Books Used:

🔗 Recommended Learning:

4. Behavioral Interviews

This engineer spent focused time on behavioral prep, writing stories tied to common questions.

✅ Strategy:

Surveyed popular behavioral questions
Matched them with STAR-based stories from past projects
Practiced repeatedly

🔗 Top Resource:

Exponent Behavioral Interview Course

⚙️ 5. ML Coding

While not always required, some companies expect ML coding rounds.

💻 Common Tasks:

Neural Network implementation
K-Means algorithm
Transformer pipeline setup

🔗 Hands-On Learning

Hugging Face Deep Learning Courses
Full Stack Deep Learning (FSDL)
Coursera TensorFlow Developer Certificate

6. Use of ChatGPT & Claude for Interview Help

From mock interviews to resume polishing and offer negotiation, LLMs played a role in their journey.

🧠 Pro Tip: Use tools like ChatGPT and Claude.ai to practice system design questions, generate STAR responses, and simulate interviews.

Summary: Key Takeaways

Pattern-based prep works — especially for ML & coding interviews
Start early with design-heavy topics
Behavioral questions can make or break your candidacy
Tools like Exponent, DesignGurus, and ChatGPT give you an edge
Lifetime deals on AlgoMonster and ByteByteGo can be cost-effective for serious prep

Final Tip: Use Mock Interviews to Sharpen Skills

The best way to test your readiness is through real-time mock interviews. Platforms like:

✅ Exponent (Peer + Expert mocks, AI interviewer, live feedback)
✅ BugFree .ai(Great for realistic FAANG-level challenges)
✅ InterviewKickstart (Mentorship-driven learning)
✅ Educative.io’s AI Interview Simulations

… all help you practice under pressure.

Bonus: Combine AlgoMonster + LeetCode

Want a structured yet affordable way to crush DSA rounds?

Try AlgoMonster — it helps you prep using patterns, with real interview problems from Meta, Google, and Amazon. It even covers Blind 75 and Monster 50 with analysis.

Other System Design and Coding Interview and Resources you may like

All the best for your Coding Interviews, if you have any doubts or questions, feel free to ask in the comments.

P. S. — If you just want to do one thing at this moment, go join Algomonster and join ByteByteGo and start learning DSA and System Design Concepts and practice coding interviews you will thank me later. It’s one of the most comprehensive resource for coding interview now.

System Design · Coding · Behavioral · Machine Learning Interviews

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