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.
Model Selection and Hyperparameter Tuning
Covers the end to end process of choosing, training, evaluating, and optimizing machine learning models. Topics include selecting appropriate algorithm families for the task such as classification versus regression and linear versus non linear models, establishing training pipelines, and preparing data splits for training validation and testing. Explain model evaluation strategies including cross validation, stratification, and nested cross validation for unbiased hyperparameter selection, and use appropriate performance metrics. Describe hyperparameter types and their effects such as learning rate, batch size, regularization strength, tree depth, and kernel parameters. Compare and apply tuning methods including grid search, random search, Bayesian optimization, successive halving and bandit based approaches, and evolutionary or gradient based techniques. Discuss practical trade offs such as computational cost, search space design, overfitting versus underfitting, reproducibility, early stopping, and when to prefer simple heuristics or automated search. Include integration with model pipelines, logging and experiment tracking, and how to document and justify model selection and tuned hyperparameters.
End to End Machine Learning Problem Solving
Assesses the ability to run a complete machine learning workflow from problem definition through deployment and iteration. Key areas include understanding the business or research question, exploratory data analysis, data cleaning and preprocessing, feature engineering, model selection and training, evaluation and validation techniques, cross validation and experiment design, avoiding pitfalls such as data leakage and bias, tuning and iteration, production deployment considerations, monitoring and model maintenance, and knowing when to revisit earlier steps. Interviewers look for systematic thinking about metrics, reproducibility, collaboration with data engineering teams, and practical trade offs between model complexity and operational constraints.
Machine Learning and Forecasting Algorithms
An in-depth coverage of machine learning methods used for forecasting and time-series prediction, including traditional time-series models (ARIMA, SARIMA, Holt-Winters), probabilistic forecasting techniques, and modern ML approaches (Prophet, LSTM/GRU, Transformer-based forecasters). Topics include feature engineering for seasonality and trend, handling non-stationarity and exogenous variables, model evaluation for time-series (rolling-origin cross-validation, backtesting, MAE/MAPE/RMSE), uncertainty quantification, and practical deployment considerations such as retraining, monitoring, and drift detection. Applies to forecasting problems in sales, demand planning, energy, finance, and other domains.
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.
Model Evaluation and Quality Assessment
Covers evaluation methods, metrics, and quality assessment approaches for machine learning models including both predictive models and generative models. Topics include selecting appropriate metrics such as accuracy, precision, recall, F one score, area under curve for ranking, root mean square error and mean absolute percentage error for regression, and the rationale for using multiple metrics and baselines. For generative and large language models, covers automatic metrics such as BLEU, ROUGE, METEOR, semantic similarity scores, LLM based evaluation techniques, human evaluation frameworks, factuality and hallucination checking, adversarial and stress testing, error analysis, and designing scalable, cost effective evaluation pipelines and quality assurance processes.
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.
DoorDash-Specific ML Applications
Domain-specific machine learning use cases within the DoorDash platform, covering production ML lifecycle topics such as demand forecasting, driver dispatch and routing, pricing and revenue optimization, recommendations, fraud detection, and real-time optimization. Includes model development, deployment, monitoring, drift handling, and scalability considerations for ML systems in a high-velocity delivery marketplace.