Lilly Tech Systems
AI/ML Engineer Interview Guide
Everything you need to ace your AI/ML engineer interview — from the phone screen to offer negotiation. Real interview questions, battle-tested model answers, and insider knowledge of what FAANG interviewers actually look for at every level from L3 to L7.
Your Learning Path
Follow these lessons in order for complete interview preparation, or jump to the round you need to practice most.
1. The AI/ML Interview Landscape
Understand every interview stage (phone screen, coding, ML design, behavioral), role levels from L3 to L7, what each FAANG company looks for, and how to build your preparation timeline.
2. Phone Screen & Recruiter Round
Common screening questions with proven answers, how to present your ML projects compellingly, handling salary expectations, and red flags that eliminate candidates instantly.
3. ML Coding Round
What to expect in ML coding interviews, common patterns (gradient descent, decision trees, data pipelines), 5+ fully coded examples, time management, and coding style expectations.
4. ML Theory Deep Dive
Top 30 questions interviewers actually ask: bias-variance tradeoff, overfitting, regularization, cross-validation, evaluation metrics — with model answers and intuitive explanations.
5. ML System Design Round
A proven framework for ML system design (clarify, design, deep dive, trade-offs). Three complete walkthroughs: recommendation system, fraud detection, and search ranking.
6. Behavioral & Culture Fit
STAR method adapted for AI roles, 10 model answers to the hardest behavioral questions, including failed ML projects, model bias discoveries, and disagreements on technical direction.
7. Offer Evaluation & Negotiation
AI/ML compensation benchmarks by level, equity evaluation frameworks, competing offers strategy, and word-for-word negotiation scripts that work.
8. Preparation Checklist & Timeline
Complete 4-week, 8-week, and 12-week prep plans, curated resource list, mock interview tips, and a comprehensive FAQ covering the most common preparation questions.
What You'll Learn
By the end of this course, you will be able to:
Navigate Every Interview Round
Confidently handle phone screens, ML coding challenges, theory deep dives, system design sessions, and behavioral interviews with proven frameworks and real answers.
Code Under Pressure
Implement gradient descent, decision trees, and data pipelines from scratch in interview settings with clean, well-structured code that impresses interviewers.
Design ML Systems
Structure ML system design answers using a repeatable framework, covering data pipelines, model selection, serving infrastructure, monitoring, and trade-off analysis.
Maximize Your Offer
Evaluate compensation packages including base, bonus, equity, and benefits. Negotiate effectively using data-driven benchmarks and proven scripts.