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.
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.
Artificial Intelligence Fluency and Practices
Practical experience using artificial intelligence tools and model driven workflows to improve developer productivity and to prototype or build features. Areas include using large language models and code assistants, prompt engineering and prompt evaluation, automating routine development tasks, generating or augmenting code and tests, integrating model inference into applications, and designing user interactions that surface model results safely. Candidates should discuss limitations and risks such as hallucination, privacy and data governance, model evaluation and monitoring in production, cost and latency trade offs, and engineering controls such as input validation, output filtering, and reproducibility.
Debugging and Troubleshooting AI Systems
Covers systematic approaches to find and fix failures in machine learning and artificial intelligence systems. Topics include common failure modes such as poor data quality, incorrect preprocessing, label errors, data leakage, training instability, vanishing or exploding gradients, numerical precision issues, overfitting and underfitting, optimizer and hyperparameter problems, model capacity mismatch, implementation bugs, hardware and memory failures, and production environment issues. Skills and techniques include data validation and exploratory data analysis, unit tests and reproducible experiments, sanity checks and simplified models, gradient checks and plotting training dynamics, visualizing predictions and errors, ablation studies and feature importance analysis, logging and instrumentation, profiling for latency and memory, isolating components with canary or shadow deployments, rollback and mitigation strategies, monitoring for concept drift, and applying root cause analysis until the underlying cause is found. Interviewers assess the candidate on their debugging process, ability to isolate issues, use of tools and metrics for diagnosis, trade offs in fixes, and how they prevent similar failures in future iterations.
AI and Machine Learning Background
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.