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.
Machine Learning Algorithms and Theory
Core supervised and unsupervised machine learning algorithms and the theoretical principles that guide their selection and use. Covers linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, k means clustering, hierarchical clustering, principal component analysis, and anomaly detection. Topics include model selection, bias variance trade off, regularization, overfitting and underfitting, ensemble methods and why they reduce variance, computational complexity and scaling considerations, interpretability versus predictive power, common hyperparameters and tuning strategies, and practical guidance on when each algorithm is appropriate given data size, feature types, noise, and explainability requirements.
Research and Product Integration
Design and evaluation of research that balances academic rigor with real world product constraints. Topics include choosing evaluation metrics that align to user value, handling limited or biased data, respecting privacy and safety constraints, trading off model quality against computational and latency budgets, planning deployment and rollback strategies, and integrating offline research with online validation. Candidates should explain how production realities shape experimental design, evaluation protocols, and methodological trade offs.
Common Machine Learning Pitfalls and Debugging
Knowledge of frequent failure modes in machine learning projects and practical approaches to detect and resolve them. Topics include data leakage, distribution shift, class imbalance and label noise, non stationary data, reproducibility failures, metric misspecification, overfitting, and systematic debugging strategies such as targeted experiments, ablation studies, unit tests for data pipelines, experiment tracking, and production monitoring.
Core Research Expertise and Specialization
Depth of knowledge in the candidate's primary research area, such as natural language processing, computer vision, reinforcement learning, causal inference, or other specialized domains. Topics include foundational algorithms, model families and architectures, common benchmarks and datasets, evaluation protocols, domain specific challenges, and familiarity with current best practices and tooling in the area.
Neural Networks and Optimization
Covers foundational and advanced concepts in deep learning and neural network training. Includes neural network architectures such as feedforward networks, convolutional networks, and recurrent networks, activation functions like rectified linear unit, sigmoid, and hyperbolic tangent, and common loss objectives. Emphasizes the mechanics of forward propagation and backward propagation for computing gradients, and a detailed understanding of optimization algorithms including stochastic gradient descent, momentum methods, adaptive methods such as Adam and RMSprop, and historical methods such as AdaGrad. Addresses practical training challenges and solutions including vanishing and exploding gradients, careful weight initialization, batch normalization, skip connections and residual architectures, learning rate schedules, regularization techniques, and hyperparameter tuning strategies. For senior roles, includes considerations for large scale and distributed training, convergence properties, computational efficiency, mixed precision training, memory constraints, and optimization strategies for models with very large parameter counts.
Production Readiness and Real World Constraints
Describe how a research prototype is translated into a reliable production system. Discuss latency, throughput, scalability, memory and compute constraints, and techniques such as model quantization, batching, and caching. Cover robustness, monitoring, alerting, model drift detection, fallback strategies, and split testing strategies for incremental rollout. Explain trade offs between model accuracy and operational cost, privacy and regulatory constraints, and the design of retraining and deployment pipelines for maintainability and observability.
Artificial Intelligence and Machine Learning Expertise
Articulate deep expertise in one or more artificial intelligence and machine learning domains relevant to the role. Cover areas such as neural network architecture design, deep learning systems, natural language processing and large language models, generative artificial intelligence, computer vision, reinforcement learning, and full stack machine learning systems. Describe specific projects and products, datasets and data pipelines, model selection and evaluation strategies, performance metrics, experimentation and ablation studies, chosen frameworks and tooling, productionization and deployment experience, scalability and inference optimization, monitoring and maintenance practices, and contributions to model interpretability and bias mitigation. Explain the measurable impact of your work on product outcomes or research goals, trade offs you managed, and how your specialization aligns to the hiring organization needs.
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 Architecture Selection and Tradeoffs
Deals with selecting machine learning or model architectures and evaluating relevant tradeoffs for a given problem. Candidates should explain how model choices affect accuracy, latency, throughput, training and inference cost, data requirements, explainability, and deployment complexity. The topic covers comparing architecture families and variants in different domains such as natural language processing, computer vision, and tabular data, for example sequence models versus transformer based models or large models versus lightweight models. Interviewers may probe metrics for evaluation, capacity and generalization considerations, hardware and inference constraints, and justification for the final architecture choice given product and operational constraints.