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.
Online Experimentation and Model Validation
Running experiments in production to validate model changes and measure business impact. Topics include splitting traffic across model variants canary deployments and champion challenger testing selecting metrics that capture both model performance and business outcomes performing sample size and test duration calculations accounting for statistical power and multiple testing adjustments and handling instrumentation and novelty bias. Candidates should be able to analyze heterogeneous treatment effects monitor experiments in real time and design ramping plans and rollback guardrails to protect user experience and business metrics. The topic also covers decision rules for when to rely on offline evaluation versus online experiments and how to interpret differences between offline model metrics and live user outcomes as part of model validation and deployment strategy.
Generative Models and Architectures
Covers the fundamentals of how generative models are constructed and trained, including types such as variational autoencoders, generative adversarial networks, diffusion models, and large language models. Includes core concepts like attention and the transformer architecture, self supervised training objectives such as next token prediction, tokenization, scaling laws, and differences between generative and discriminative approaches. Also addresses practical techniques for adapting and improving models including fine tuning, transfer learning, prompt engineering, few shot and zero shot learning, inference trade offs, model compression, and deployment considerations such as latency, memory, and cost. Evaluation topics include likelihood based metrics and practical applied evaluation methods for generation quality.
Meta AI & ML Strategy
Overview of Meta's AI and ML strategic direction, governance, research investments, platform capabilities, responsible AI initiatives, and how these strategies shape engineering choices and product development at scale.
Model Deployment and Serving
Covers techniques and practices for deploying machine learning models and serving predictions to downstream systems or users. Key areas include selection among batch inference, real time inference, and streaming inference based on trade offs such as latency, throughput, cost, and prediction staleness; common serving architectures and where they are appropriate including dedicated inference services, serverless functions, and edge deployment; deployment strategies for safe releases such as canary, shadow, blue green, and rolling updates; packaging and operationalization practices including containerization, orchestration, model artifacts, model versioning, and model registry practices; scaling and performance considerations such as batching and micro batching, autoscaling, hardware acceleration and model optimization techniques; interface and integration concerns including request and response formats for application programming interfaces, timeouts and retry policies, and online versus offline feature pipelines and feature serving; validation and experimentation such as A and B experiments for live validation, metrics for rollout decisions, and monitoring for model performance degradation and data drift; and integration with continuous integration and continuous deployment pipelines including automated model tests, validation gates, rollout automation and rollback strategies. For junior candidates, expect discussion of trade offs between approaches, recognition of appropriate choices given constraints, and an understanding of a basic deployment architecture.
Multi Armed Bandits and Experimentation
Covers adaptive experimentation methods that trade off exploration and exploitation to optimize sequential decision making, and how they compare to traditional A B testing. Core concepts include the exploration versus exploitation dilemma, regret minimization, reward modeling, and handling delayed or noisy feedback. Familiar algorithms and families to understand are epsilon greedy, Upper Confidence Bound, Thompson sampling, and contextual bandit extensions that incorporate features or user context. Practical considerations include when to choose bandit approaches versus fixed randomized experiments, designing reward signals and metrics, dealing with non stationary environments and concept drift, safety and business constraints on exploration, offline evaluation and simulation, hyperparameter selection and tuning, deployment patterns for online learning, and reporting and interpretability of adaptive experiments. Applications include personalization, recommendation systems, online testing, dynamic pricing, and resource allocation.
ML Pipeline and Workflow Orchestration
Understanding ML pipelines: automated workflows for data → preprocessing → training → evaluation → deployment. Benefits: reproducibility, automation, reliability. Basic familiarity with concepts like DAGs (directed acyclic graphs), dependencies, and triggering. Knowing that effective teams automate these processes.
Types of Machine Learning
Covers the main paradigms of machine learning and when to apply each. Supervised learning entails training models on labeled examples to predict outputs and includes tasks such as regression for continuous targets and classification for discrete targets. Candidates should understand common supervised algorithms including linear regression, logistic regression, decision trees, random forests, and support vector machines, as well as how to evaluate them using metrics such as accuracy, precision, recall, the harmonic mean of precision and recall, mean squared error, and area under the receiver operating characteristic curve. Unsupervised learning involves finding structure in unlabeled data through methods such as clustering and dimensionality reduction; examples include k means clustering, hierarchical clustering, principal component analysis, and autoencoders, and evaluation approaches include silhouette score, reconstruction error, and qualitative inspection. Reinforcement learning involves agents that learn policies through interaction with an environment and reward signals, exemplified by methods such as Q learning and policy gradient approaches; for junior roles a basic conceptual understanding is sufficient. Candidates should be able to give real world examples for each paradigm, discuss trade offs such as the cost and availability of labeled data, the interpretability of models, sample efficiency, and typical failure modes, and explain how to choose an approach for a given problem and dataset.
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.
Production Machine Learning Systems
Design, build, deploy, and operate end to end machine learning systems in production. Topics include data ingestion and validation, feature engineering and real time feature computation, training and testing pipelines, model serving and prediction latency optimization, scalability and reliability of infrastructure, and monitoring and observability for data and model performance. Covers detection and handling of data drift and model drift, retraining strategies and automation, versioning and reproducibility for data code and models, experiment tracking and model registries, and practices for continuous integration and continuous delivery in machine learning contexts. At senior and staff levels, expect system level trade offs, designing platform capabilities for multiple teams, debugging production performance regressions, and managing technical debt in machine learning systems.