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.
Emerging Technologies and Live Streaming
Candidates should describe how emerging technologies such as artificial intelligence, machine learning, and generative artificial intelligence can be applied in the streaming domain, and how to integrate live content capabilities at scale. Discussion should include candidate use cases for personalization, content generation and augmentation, automated metadata extraction, encoding optimization, moderation, model training data pipelines, model serving and online inference, latency and resource trade offs for real time features, experiment and validation infrastructure, and privacy safety and fairness considerations. For live streaming, cover low latency ingestion and distribution, real time bitrate adaptation, client synchronization, moderation and content safety, and failover strategies. Interviewers assess the candidate's ability to balance innovation with operational stability, data needs, and regulatory risk.
Artificial Intelligence and Machine Learning Strategy
Evaluate the candidate's approach to identifying evaluating and implementing artificial intelligence and machine learning opportunities across products and operations. Topics include use case framing and prioritization based on business impact data availability and technical feasibility; data strategy feature pipelines and annotation; model development lifecycle and experimentation practices; model training infrastructure model serving and deployment patterns; continuous integration and continuous delivery for models and monitoring for performance and data drift; model governance including fairness explainability and privacy; cost and latency trade offs for inference and choices between cloud and edge; organizational models such as centralized platform teams versus embedded product teams; tooling and vendor selection; and metrics for measuring model return on investment. Interviewers should probe experience with model registries versioning deployment rollbacks A B testing and operationalizing models in production.
Recommendation and Ranking Systems
Designing recommendation and ranking systems and personalization architectures covers algorithms, end to end system architecture, evaluation, and operational concerns for producing ranked item lists that meet business and user objectives. Core algorithmic approaches include collaborative filtering, content based filtering, hybrid methods, session based and sequence models, representation learning and embedding based retrieval, and learning to rank models such as gradient boosted trees and deep neural networks. At scale, common architectures use a two stage pipeline of candidate retrieval followed by a ranking stage, supported by approximate nearest neighbor indexes for retrieval and low latency model serving for ranking. Key engineering topics include feature engineering and feature freshness, offline batch pipelines and online incremental updates, feature stores, model training and deployment, caching and latency optimizations, throughput and cost trade offs, and monitoring and model governance. Evaluation spans offline metrics such as precision at k, recall at k, normalized discounted cumulative gain, calibration and bias checks, plus online metrics such as engagement, click through rate, conversion and revenue and longer term retention. Important product and research trade offs include accuracy versus diversity and novelty, fairness and bias mitigation, popularity bias and freshness, cold start for new users and items, exploration and exploitation strategies, multi objective optimization and business constraint balancing. Operational considerations for senior level roles include scaling to millions of users and items, experiment design and split testing, addressing feedback loops and data leakage, interpretability and explainability, privacy and data minimization, and aligning recommendation objectives to business goals.
Personalization and Ranking Systems
Designing personalization and ranking architectures that operate at very large scale. Candidates should cover candidate generation and ranking pipelines, offline and real time feature engineering, feature stores, model training and serving, learning to rank approaches, latency and freshness tradeoffs, using in memory structures such as prefix tries for fast type ahead, experimentation and A B testing infrastructure, online evaluation and feedback loops, and data privacy and governance concerns.
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.