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.
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.
Model Training Infrastructure and Experimentation
Design infrastructure and workflows to train machine learning models at scale and enable rapid experimentation. Core areas include distributed training strategies such as data parallelism model parallelism and pipeline parallelism; hardware and instance selection including graphics processing units and tensor processing units; efficient resource scheduling and autoscaling for training; hyperparameter tuning at scale using grid search random search and Bayesian optimization; experiment and metadata tracking, reproducibility and checkpointing, resume and fault tolerance strategies; pipeline automation, containerized reproducible training environments, dataset management, and trade offs between training speed cost and model quality to support iterative model development.
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.
Model Deployment and Inference Optimization
Comprehensive coverage of designing, deploying, and operating systems that serve machine learning models in production while optimizing inference for latency, throughput, reliability, cost, and resource constraints. Topics include serving architectures such as batch processing, streaming, real time online serving, and edge inference, trade offs between precomputation and on demand computation, and deployment topologies for cloud, on premise servers, and edge devices. Discuss model versioning and rollout patterns including canary rollouts, blue green deployments, gradual rollouts, A B testing, and rollback strategies, and the infrastructure to support them such as containerization, orchestration, routing, traffic management, load balancing, and autoscaling. Cover inference optimization techniques including quantization, pruning, knowledge distillation, model compression, efficient architecture choices for computer vision and large language models, model format export and compatibility such as Open Neural Network Exchange and saved model formats, runtime optimizations, batching, request coalescing, caching, pipelining, and handling heterogeneous models and large model inference. Include hardware and infrastructure considerations such as graphics processing units, tensor processing units and other accelerators, memory and latency budgets, distributed and accelerated inference strategies, cost and energy trade offs, and edge device constraints. Operational and observability concerns include logging, metrics, latency and error tracking, model drift and data drift detection, profiling and benchmarking, performance regression alerts, debugging predictions in production, integration with continuous integration and continuous delivery pipelines, automated retraining and rollback policies, and practices to enable reliable, observable, and rapid iteration at senior and staff levels. For vision specific deployment, address image preprocessing pipelines, model input and output formats, and edge constraints such as energy and memory limits.
Feature Engineering and Selection
Covers the end to end process of transforming raw data into predictive and stable model inputs and choosing the most useful subset of those inputs. Topics include generating features from domain signals and timestamps, numerical transformations such as scaling binning and logarithmic transforms, categorical encodings including one hot and target encoding, creation of interaction and polynomial features, construction of dense feature vectors for model consumption, handling missing values and outliers, and strategies for class imbalance. Also includes feature selection and dimensionality reduction methods such as filter techniques statistical tests wrapper methods embedded model based selection mutual information analysis and tree based importance measures. Emphasis is placed on avoiding data leakage validating feature stability over time interpreting feature contributions and documenting rationale for feature creation or removal. For senior roles include designing feature engineering best practices mentoring others and considering feature impact on model interpretability and business metrics.