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.
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.
Large Scale Distributed Training and Parallel Computing
Understand strategies for training models at scale: data parallelism, model parallelism, pipeline parallelism, and hybrid approaches. Address synchronization, gradient compression, all-reduce operations, and communication efficiency. Discuss handling hardware failures, reproducibility, and memory/compute trade-offs. For Staff-level, discuss training 100B+ parameter models.
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.
Production Machine Learning Infrastructure
Covers the design, deployment, and operation of machine learning systems in production. Topics include distributed training at scale, model serving and inference architectures, optimizing inference for latency and throughput, hardware and accelerator utilization, deployment patterns such as canary rollouts and staged rollouts, model versioning and registries, feature stores and reliable data pipelines, observability and alerting for model performance and data drift, autoscaling and cost trade offs, and integration with continuous integration and continuous delivery pipelines and governance processes.
Research to Production Pipeline
Describe how research prototypes become reliable production services and the engineering and organizational work required. Topics include experiment reproducibility, data and feature pipeline design, model packaging and versioning, serving infrastructure choices, canary and shadow deployments, monitoring and alerting for data and performance drift, retraining orchestration, rollback and recovery procedures, and measuring real world impact. Include practical handoffs between research, engineering, and product teams and considerations for cost, latency, and compliance.
ML System Integration and Monitoring
Consider how algorithmic and machine learning solutions integrate into production systems end to end. Coverage includes model and feature serving infrastructure, feature pipelines and feature stores, latency and throughput budgets, instrumentation and observability, metric design and alerting, model versioning and rollback, canary and shadow deployments, feedback loops between serving and data collection, trade offs between model complexity and operational constraints, capacity planning and cost control, and practices for ensuring reliability and debugging in production.
Objective Function and Stakeholder Tradeoffs
Describe how to design objective functions and evaluation criteria that balance multiple stakeholder needs in a marketplace or product system. Topics include defining utility or loss functions for customers merchants and service providers; multi objective optimization and weighting strategies; constrained optimization and Pareto tradeoffs; incorporating fairness and equity constraints; choosing proxies and business facing metrics versus operational signals; reward shaping and regularization to avoid pathological incentives; robust objectives under distribution shift; and methods to compare and validate objective choices via simulation counterfactual evaluation offline metrics and live experiments.
Systems Thinking and Platform Strategy
This topic evaluates staff level systems thinking and platform strategy: the ability to reason about how a single design decision, feature, or fix ripples across multiple teams, systems, and business outcomes. Expect to discuss cross team dependencies and ownership boundaries, data quality and lineage, latency and infrastructure cost constraints, monitoring and observability, and the long term maintenance implications of design choices. Candidates should be able to prioritize when to ship a local point fix versus when to invest in a shared platform capability, and to communicate trade offs, risks, and roadmaps clearly to engineering and product stakeholders.