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.
Spotify Product Features & ML Architecture
An integrated topic covering product feature design in a music streaming service and the machine learning architecture that enables those features. It includes personalization and recommendations, feature engineering, ML model lifecycle (training, validation, deployment, serving), data pipelines and feature stores, experimentation and A/B testing, monitoring and observability, scalability and reliability considerations for ML-driven product features, and privacy/governance considerations relevant to consumer data.
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.
Large Language Model Observability and Evaluation
Covers the end to end product and technical considerations for monitoring, evaluating, and troubleshooting large language model systems. Topics include what observability means for model driven features, which signals to capture such as input provenance, token usage, latency, error modes, and outcome quality, and how to design instrumentation and data contracts that ensure consistent and auditable telemetry. It includes evaluation approaches and metrics such as relevance, accuracy, hallucination rate, calibration, and cost, and the trade offs between human labeling, automated metrics, and model driven judges. Product design aspects cover dashboards, alerts, logging, tracing, debugging interfaces, and developer workflows that make investigation and root cause analysis efficient. Finally this topic addresses operational concerns for an observability platform including storage and cost trade offs, scaling telemetry pipelines, privacy and compliance constraints, and how evaluation and observability feed back into model improvement cycles.
ML Systems Architecture & Components
Design and architecture of production-grade machine learning systems, including data ingestion and preprocessing pipelines, feature stores, model training and validation pipelines, deployment and serving infrastructure, monitoring and observability, model governance, and platform-level concerns such as scalability, reliability, security, and integration with product systems.