ml_interview_structure.md

What happens in a ML Interview?

• Background and culture fit
• Whiteboard Coding (similar to SWE interviews)
• Pair Coding (similar to SWE-interviews)
• Pair debugging (often ML-specific code)
• Math puzzles (e.g. involving Linear Algebra)
• Take-home ML project
• Applied ML (e.g. explain how you'd solve this problem with ML)
• Previous ML projects (e.g. probing on what you tried, why things did/didn't work)
• ML Theory (e.g. bias-variance tradeoff, overfitting, underfitting, understanding of specific algorithms)
• Deploy a pre-trained ML model
• API integrations
• Here’s a description of a component from our stack. Could you re-implement a toy version of it for me in one hour?

How to Stand out for ML roles?

• Build SWE skills (e.g. work at a well-known software company)
• Exhibit interest in ML (e.g. conference attendance, online courses taken)
• Show you have broad knowledge of ML (e.g. write blog posts synthesizing a research area)
• Demonstrate ability to get ML projects done (e.g. create side projects, re-implement papers)
• Prove you can think creatively in ML (e.g. win Kaggle Competitions, publish papers)

How to prepare for the interview?

• Prepare for a general SWE interview (e.g. Cracking the Coding Interview)
• Prepare to talk in detail about your past ML projects (remember details, prepare to talk about tradeoffs and decisions you made)
• Review how basic ML algorithms work (linear/logistic regression, nearest neighbor, decision trees, k-means, MLPs, ConvNets, reccurent nets, etc)
• Review ML theory
• Think about the problems the company you're interviewing with may face and what ML techniques may apply to them

Structure

Goal: Be a good fit for the team – research everything about it

1. Behavioral
2. Math, Stats, ML, CS
3. Coding Algorithm/Data Structures Interview
4. ML/Software System Design, Open-ended Problems
5. Culture

(1) Recruiter

• Screen Questions
• Coding
• ML Fundamnetals
• Why do you want this job & why are you a good fit?

(2) Computer Science 2.1 Data Structures

• Vectors, Arrays, Lists, Strings
• Trees, Binary Trees, Balanced Trees
• Tries & Graphs
• Heaps
• Hash Table
• Linked Lists
• Queue and Stack
• Divide and Conquer

2.2 Algorithms

• Sorting
• Searching
• Breadth-First Search
• Depth-First Search
• Binary Search
• Merge Sort
• Quick Sort
• Greedy
• Game Theory
• NP Complete
• Constructive Algorithms

2.3 Concepts

• Bit Manipulation
• Memory (Stack vs. Heap)
• Recursion
• Dynamic Programming
• Big O Time & Space

2.4 Programming Fundamentals

• Variables
• Functions
• Conditional Statements
• Lists
• Loops
• Dictionaries
• Objects
• Interfaces

2.5 Software Engineering Practices

• Automation/Unit/Integration/Security/Compatibility Tests
• Test-Driven Development
• Pillars of OOP/FP
• Multi-threading
• SOLID Principles
• Overriding and Overloading
• Static & Dynamic Binding
• Software Design & Patterns
• Continuous Integration/Continuous Development
• High Quality, Reusable, Clean, Scalable Code
• A/B and Multi-Variate Testing

2.6 Database:

• Tools: SQL, MySQL, NoSQL
• Joins
• Indexing
• Queries
• Subqueries
• ACID Properties

(3) Artificial Intelligence 3.1 Statistics

• Central Limit Theorom
• Mean, Variance, Stat fundamentals
• Simpsons Paradox
• Dispersion, Correlation, Normal Distribution, Hypothesis Testing
• KL Divergence
• Covariance/Domain/Concept/Prior Probability/Spurious Correlation shift

3.2 Neural Networks

• NNs, CNNs, RNNs, GNNs, GANs
• LSTMs

3.3 Machine Learning

• Supervised/Unsupervised Learning
• Overfitting/Underfitting
• Boosting
• Regularization
• Normalization
• Loss Functions
• Optimizers
• Gradient Descent
• Debugging ML models
• Fine Tuning
• Classification & Regression
• Clustering
• Dimensionality Reduction
• Model Compression Techniques

3.4 Algorithms

• Support Vector Machine
• XGBoost
• Random Forest
• Linear & Logistic Regression
• PCA
• K-means
• K-Nearest Neighbours
• Decision Trees
• Neural Networks

3.5 Computer Vision

• CNNs, Visual Transformers
• Filter, steps
• Models: VGG, AlexNet, Inception, LeNet-5, AlexNet, GoogLeNet, ResNet, SENet, Xception, ResNeXt, DenseNet, MobileNet, CSPNet, and EfficientNet.
• Object Detection: YOLO
• Segmentation: FCN, Mask R-CNN, Fast R-CNN
• Object Tracking: DeepSort

3.6 Natural Language Processing

• Word Embeddings
• Recurrent Networks
• Attention Networks
• Transformers
• Bidirectional Models
• Beam Search
• Language Models
• Machine Translation
• N-gram Models

3.7 Recommender Systems

• Content and Basic Collaborative Filtering based Recommender Systems

3.8 ML Research

• In-depth on few papers like ImageNet, GANs

(4) Systems Design 4.1 ML Systems and Infra

• Lambda
• Data Versioning, Storage, Compute, Analytics
• Using data from DataBricks, Snowflake
• Parallel Processing
• Edge/Tiny/Cloud ML
• Batch/Online Prediction
• Online/Continual Learning
• Containerization using Docker/Kubernetes
• Data Labelling Methods
• Elastic Scale
• Feature Stores, ETL
• Model Monitoring Accuracy, Automated Re-training Workflows
• Metadata Storage & Management
• Active Learning
• JSON, Embeddings

4.2 Concepts

• Data/Concept Shift/Lag Detection
• Adverserial Validation
• Databases/Warehouses/Lakes
• Reproducible ML

4.3 Software Systems Design

• Load Balancing
• Redundancy
• Backend/Database

4.4 MLOps Cycle

• Model Serving
• Reproducibility of Results
• Data Labelling
• Distributed Training
• Model Re-training
• Online and Continual Learning
• Feature Stores
• ML Monitoring and Deployment

(5) Case Study

• Recomender Systems
• Machine Tranlsation
• Search
• Forecasting

(6) Behavioural

• Why do you want to work here? What do you want to contribute?
• Why should we hire you?