ML/AI Interview Prep Guide
Complete preparation for Machine Learning and AI engineering interviews. Covers ML theory fundamentals, model design and evaluation, ML system design at scale, and practical coding challenges. Designed for MLE, Applied Scientist, and AI Engineer roles at top tech companies and AI-first startups.
Key Features
- 75 ML theory questions with rigorous explanations and mathematical intuition
- 12 model design scenarios — from problem framing to deployment strategy
- 8 ML system design problems with production architecture diagrams
- 30 coding challenges in Python (NumPy, pandas, scikit-learn patterns)
- Statistics & probability primer — the 20% of stats that covers 80% of interviews
- Paper discussion prep — how to present and critique research papers
Content Breakdown
| Section | Items | Difficulty Range |
|---|---|---|
| ML Theory & Fundamentals | 75 | ★ to ★★★★ |
| Model Design | 12 | ★★★ to ★★★★★ |
| ML System Design | 8 | ★★★ to ★★★★★ |
| Coding Challenges | 30 | ★★ to ★★★★ |
| Statistics & Probability | 20 | ★★ to ★★★ |
| Paper Discussion | 5 | ★★★★ |
Sample Content
ML Theory: Bias-Variance Tradeoff
Question: Explain the bias-variance tradeoff. How do you diagnose whether a model suffers from high bias or high variance?
Answer:
Total Error = Bias² + Variance + Irreducible Noise
High Bias (underfitting):
- Training error: HIGH
- Validation error: HIGH
- Gap between them: SMALL
- Fix: more features, more complex model, less regularization
High Variance (overfitting):
- Training error: LOW
- Validation error: HIGH
- Gap between them: LARGE
- Fix: more data, regularization, simpler model, dropout, ensemble
Diagnostic Tool: Learning Curves
- Plot training and validation error vs training set size
- High bias: both curves plateau at high error
- High variance: training error low, validation error high, gap persists
Model Design Scenario: Content Recommendation
Prompt: Design a content recommendation system for a news aggregator app with 5M daily active users and 50K new articles per day.
Problem Framing:
Task: Ranking (predict P(click | user, article))
Metric: NDCG@10 (ranking quality), CTR (business metric)
Feature Engineering:
User features: reading history, topic preferences, time-of-day patterns
Article features: topic embedding, recency, source quality, length
Interaction: user-topic affinity scores, collaborative filtering signals
Architecture:
┌─────────────┐ ┌──────────────┐ ┌──────────────┐
│ Candidate │──▶│ Ranking │──▶│ Re-ranking │
│ Generation │ │ Model │ │ (diversity, │
│ (ANN/HNSW) │ │ (deep model)│ │ freshness) │
│ ~1000 items │ │ → top 50 │ │ → top 10 │
└─────────────┘ └──────────────┘ └──────────────┘
Candidate Gen: Two-tower model (user tower + item tower)
Ranking: Deep neural network with cross-features
Re-ranking: Business rules (diversity, deduplication, freshness boost)
Evaluation:
Offline: NDCG@10, AUC, calibration plots
Online: A/B test measuring CTR, session duration, next-day retention
Coding Challenge: Implement K-Means from Scratch
import numpy as np
def kmeans(X: np.ndarray, k: int, max_iters: int = 100) -> tuple:
"""K-Means clustering from scratch.
Args:
X: Data matrix of shape (n_samples, n_features)
k: Number of clusters
max_iters: Maximum iterations
Returns:
centroids: Final cluster centers (k, n_features)
labels: Cluster assignment for each point (n_samples,)
"""
n_samples = X.shape[0]
# Initialize centroids randomly from data points
indices = np.random.choice(n_samples, k, replace=False)
centroids = X[indices].copy()
for _ in range(max_iters):
# Assign each point to nearest centroid
distances = np.linalg.norm(X[:, np.newaxis] - centroids, axis=2)
labels = np.argmin(distances, axis=1)
# Update centroids
new_centroids = np.array([
X[labels == i].mean(axis=0) if np.any(labels == i)
else centroids[i]
for i in range(k)
])
# Check convergence
if np.allclose(centroids, new_centroids):
break
centroids = new_centroids
return centroids, labels
Follow-up: What's the time complexity? How would you handle empty clusters? How does K-Means++ improve initialization?
Study Plan
| Week | Focus | Daily Time |
|---|---|---|
| 1 | Statistics & probability fundamentals | 45 min |
| 2 | ML theory: supervised learning, loss functions, regularization | 60 min |
| 3 | ML theory: trees, ensembles, deep learning basics | 60 min |
| 4 | Model design: problem framing, feature engineering, evaluation | 60 min |
| 5 | ML system design: training pipelines, serving, monitoring | 60 min |
| 6 | Coding: implement algorithms from scratch (k-means, logistic reg, decision tree) | 75 min |
| 7 | Advanced: NLP, embeddings, recommendation systems | 60 min |
| 8 | Mock interviews + paper discussions | 60 min |
Practice Tips
- Explain like you're teaching. Interviewers want you to demonstrate understanding, not recite definitions.
- Always discuss tradeoffs. Precision vs recall, online vs batch, complexity vs interpretability.
- Know your evaluation metrics cold. AUC, F1, NDCG, RMSE — when to use which and why.
- Practice ML system design separately. It's a distinct skill from model design.
- Implement from scratch at least once. Logistic regression, decision tree, and k-means at minimum.
Contents
-
src/— Theory questions, model design scenarios, coding challenges -
examples/— Complete solutions with mathematical derivations -
docs/— Statistics primer, system design templates, paper discussion guide
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