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.
Applied ML to Real-World Problems and Constraints
Practical application of machine learning to solve real-world problems while navigating operational constraints such as latency and compute budgets, data privacy and regulatory requirements, fairness, interpretability, and production readiness. Covers problem formulation, data collection and preprocessing under real-world data limitations, feature engineering, model selection and evaluation for constrained settings, deployment patterns (online vs. batch/offline), monitoring and retraining, ML platform design, and governance for responsible AI.
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.
Machine Learning Fundamentals
Core machine learning concepts and terminology for conceptual understanding. Topics include supervised and unsupervised learning, regression and classification problems, training validation and test splits, cross validation, loss functions and optimization at a high level, model evaluation metrics, overfitting and underfitting, regularization concepts, and common basic model families such as linear models decision trees nearest neighbors and simple neural networks. Emphasis is on conceptual explanations and trade offs rather than deep mathematical derivations.
Model Evaluation and Validation
Comprehensive coverage of how to measure, validate, debug, and monitor machine learning model performance across problem types and throughout the development lifecycle. Candidates should be able to select and justify appropriate evaluation metrics for classification, regression, object detection, and natural language tasks, including accuracy, precision, recall, F one score, receiver operating characteristic area under the curve, mean squared error, mean absolute error, root mean squared error, R squared, intersection over union, and mean average precision, and to describe language task metrics such as token overlap and perplexity. They should be able to interpret confusion matrices and calibration, perform threshold selection and cost sensitive decision analysis, and explain the business implications of false positives and false negatives. Validation and testing strategies include train test split, holdout test sets, k fold cross validation, stratified sampling, and temporal splits for time series, as well as baseline comparisons, champion challenger evaluation, offline versus online evaluation, and online randomized experiments. Candidates should demonstrate techniques to detect and mitigate overfitting and underfitting including learning curves, validation curves, regularization, early stopping, data augmentation, and class imbalance handling, and should be able to debug failing models by investigating data quality, label noise, feature engineering, model training dynamics, and evaluation leakage. The topic also covers model interpretability and limitations, robustness and adversarial considerations, fairness and bias assessment, continuous validation and monitoring in production for concept drift and data drift, practical testing approaches including unit tests for preprocessing and integration tests for pipelines, monitoring and alerting, and producing clear metric reporting tied to business objectives.