ML Engineer — Interview Ready
The 8-week ramp to walking into interviews confident
You'll answer the questions interviewers actually ask across LLMs, RAG, classical ML, systems, and ethics — with senior signals baked in.
You've done some ML work and have interviews coming up (or want to be ready when they do).
- You've trained at least one model.
- Comfortable with Python and basic ML vocabulary.
Complete a full mock interview cycle (fundamentals + system design + behavioral) and pass a real one.
- 1
Stage 1: Nail the LLM fundamentals
Every ML interview in 2026 opens here. Miss these and the rest doesn't matter.
Deliverable · Score 90%+ on the LLM Fundamentals quiz — Junior + Mid combined. - 2
Stage 2: Prompting & structured output
Interviewers probe this because it separates candidates who've built things from ones who've read about them.
Deliverable · Explain few-shot, CoT, self-consistency, and structured output — out loud — in under 60 seconds each. - 3
Stage 3: RAG + embeddings
The most-asked applied topic. If you can't design a RAG system on a whiteboard, this stage is why.
- Course: Building LLM Apps with RAG
- Interview hub: RAG (10 questions)
- Interview hub: Embeddings (8 questions)
- Quiz yourself: RAG + Embeddings
Deliverable · Whiteboard a RAG system for company docs, end-to-end, from ingest to serving. - 4
Stage 4: Classical ML you'll still be asked about
Bias-variance, overfitting, metrics, regularization — the classics stay in the interview loop.
- Course: Machine Learning Foundations
- Interview hub: Classical ML (17 questions)
- Interview hub: Evaluation (11 questions)
Deliverable · Explain the bias-variance tradeoff and pick a metric for an imbalanced problem — out loud. - 5
Stage 5: Deep learning & transformers
Self-attention, positional encoding, the training loop — the questions that separate mid from senior.
- Course: Deep Learning Essentials
- Interview hub: Transformers & DL (16 questions)
- Quiz yourself: Transformers (Senior only)
Deliverable · Derive self-attention on paper. Explain why we scale by sqrt(d_k). - 6
Stage 6: Agents, fine-tuning, evaluation
The 2026 additions to every ML interview. Show you've kept up.
- Course: AI Agents from Scratch
- Interview hub: Agents (15 questions)
- Interview hub: Fine-tuning (12 questions)
Deliverable · Explain when NOT to use an agent, LoRA vs full fine-tuning, and how to measure hallucination — cleanly. - 7
Stage 7: ML system design + MLOps
This is where offers are won. Design questions test judgment more than knowledge.
Deliverable · Design a RAG chatbot and a semantic-search system on a whiteboard, with tradeoffs stated. - 8
Stage 8: Ethics, safety, and the human side
Increasingly asked — and a great place to sound senior.
Deliverable · Ace one full mock interview. Then take a real one.