Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Machine Learning Algorithms and Theory
Core supervised and unsupervised machine learning algorithms and the theoretical principles that guide their selection and use. Covers linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, k means clustering, hierarchical clustering, principal component analysis, and anomaly detection. Topics include model selection, bias variance trade off, regularization, overfitting and underfitting, ensemble methods and why they reduce variance, computational complexity and scaling considerations, interpretability versus predictive power, common hyperparameters and tuning strategies, and practical guidance on when each algorithm is appropriate given data size, feature types, noise, and explainability requirements.
Artificial Intelligence Projects and Problem Solving
Detailed discussion of artificial intelligence and machine learning projects you have designed, implemented, or contributed to. Candidates should explain the problem definition and success criteria, data collection and preprocessing, feature engineering, model selection and justification, training and validation methodology, evaluation metrics and baselines, hyperparameter tuning and experiments, deployment and monitoring considerations, scalability and performance trade offs, and ethical and data privacy concerns. If practical projects are limited, rigorous coursework or replicable experiments may be discussed instead. Interviewers will assess your problem solving process, ability to measure success, and what you learned from experiments and failures.
Linear and Logistic Regression Implementation
Covers the fundamentals and implementation details of linear regression for continuous prediction and logistic regression for binary or multiclass classification. Candidates should understand model formulation, hypothesis functions, and the intuition behind fitting a line or hyperplane for regression and using a sigmoid or softmax function for classification. Include loss functions such as mean squared error for regression and cross entropy loss for classification, optimization methods including gradient descent and variants, regularization techniques, feature engineering and scaling, metrics for evaluation such as mean absolute error and accuracy and area under curve, and hyperparameter selection and validation strategies. Expect discussion of practical implementation using numerical libraries and machine learning toolkits, trade offs and limitations of each approach, numerical stability, and common pitfalls such as underfitting and overfitting.
Feature Engineering and Feature Stores
Designing, building, and operating feature engineering pipelines and feature store platforms that enable large scale machine learning. Core skills include feature design and selection, offline and online feature computation, batch versus real time ingestion and serving, storage and serving architectures, client libraries and serving APIs, materialization strategies and caching, and ensuring consistent feature semantics and training to serving consistency. Candidates should understand feature freshness and staleness tradeoffs, feature versioning and lineage, dependency graphs for feature computation, cost aware and incremental computation strategies, and techniques to prevent label leakage and data leakage. At scale this also covers lifecycle management for thousands to millions of features, orchestration and scheduling, validation and quality gates for features, monitoring and observability of feature pipelines, and metadata governance, discoverability, and access control. For senior and staff levels, evaluate platform design across multiple teams including feature reuse and sharing, feature catalogs and discoverability, handling metric collision and naming collisions, data governance and auditability, service level objectives and guarantees for serving and materialization, client library and API design, feature promotion and versioning workflows, and compliance and privacy considerations.
Imbalanced Classification in Security
Comprehensive coverage of applying classification methods to security-related datasets with severe class imbalance. Topics include traditional machine learning classifiers (logistic regression, SVM, decision trees, random forests, gradient boosting), loss functions for imbalance (focal loss, class-weighted loss, symmetric cross-entropy), and data- or algorithm-level techniques (SMOTE, undersampling, stratified sampling, instance weighting, threshold adjustment). Includes ensemble approaches for imbalance (balanced random forests, cascade/classifier ensembles), trade-offs between precision, recall, and computational cost, and practical guidelines for selecting methods in security domains such as intrusion detection, malware classification, fraud detection, and threat analytics.
Feature Engineering & Selection Basics
Understand why features matter and basic techniques: scaling/normalization, handling categorical variables (one-hot encoding, label encoding), creating interaction features, and feature importance. Know that good features are as important as good algorithms. Understand why feature scaling matters for algorithms like KNN or linear models.
Handling Class Imbalance
Addressing scenarios where one class significantly outnumbers others (common in fraud, churn, disease detection). Problems: accuracy becomes misleading (95% accuracy can be trivial if 95% are negative class), model biased toward majority class. Solutions: Resampling (undersampling majority, oversampling minority, or SMOTE), adjusting class weights in loss function, choosing appropriate metrics (F1, precision-recall instead of accuracy), ensemble methods. For junior level, recognize imbalance problems, understand why accuracy fails, and know multiple approaches to handling it.
Data Organization and Infrastructure Challenges
Demonstrate knowledge of the technical and operational problems faced by large scale data and machine learning teams, including data infrastructure scaling, data quality and governance, model deployment and monitoring in production, MLOps practices, technical debt, standardization across teams, balancing experimentation with reliability, and responsible artificial intelligence considerations. Discuss relevant tooling, architectures, monitoring strategies, trade offs between innovation and stability, and examples of how to operationalize models and data products at scale.
Model Evaluation and Validation
Comprehensive coverage of how to measure, validate, debug, and monitor machine learning model performance across problem types and throughout the development lifecycle. Candidates should be able to select and justify appropriate evaluation metrics for classification, regression, object detection, and natural language tasks, including accuracy, precision, recall, F one score, receiver operating characteristic area under the curve, mean squared error, mean absolute error, root mean squared error, R squared, intersection over union, and mean average precision, and to describe language task metrics such as token overlap and perplexity. They should be able to interpret confusion matrices and calibration, perform threshold selection and cost sensitive decision analysis, and explain the business implications of false positives and false negatives. Validation and testing strategies include train test split, holdout test sets, k fold cross validation, stratified sampling, and temporal splits for time series, as well as baseline comparisons, champion challenger evaluation, offline versus online evaluation, and online randomized experiments. Candidates should demonstrate techniques to detect and mitigate overfitting and underfitting including learning curves, validation curves, regularization, early stopping, data augmentation, and class imbalance handling, and should be able to debug failing models by investigating data quality, label noise, feature engineering, model training dynamics, and evaluation leakage. The topic also covers model interpretability and limitations, robustness and adversarial considerations, fairness and bias assessment, continuous validation and monitoring in production for concept drift and data drift, practical testing approaches including unit tests for preprocessing and integration tests for pipelines, monitoring and alerting, and producing clear metric reporting tied to business objectives.