FAANG‑style prompts for prep

A social post this month turned interview prep into a production drill: candidates are being asked to design, ship, and monitor machine learning systems, not just pick models. (x.com) The post, from X user bigaiguy, listed role-play prompts with company labels such as “Uber MLOps Production Engineer” and “Meta AI Recommender Systems Builder.” The examples asked for concrete steps around model packaging, Docker containers, deployment, monitoring, and A/B testing. (x.com) Machine learning in production is the part that starts after training: teams have to move a model into a live service, feed it fresh data, and check whether its predictions still hold up. Google’s Machine Learning Crash Course says production systems need deployment testing, monitoring, and checks for training-serving skew, the mismatch between training data and live inputs. (developers.google.com 1) (developers.google.com 2) That framing matches how large companies describe their own work. Uber said its Michelangelo platform was built to cover the end-to-end workflow — data, training, evaluation, deployment, prediction, and monitoring — because teams previously had no standard path to put models into production. (uber.com) Meta describes production engineering as a hybrid software-and-systems role focused on reliability, scalability, performance, and security. In a December 19, 2023 engineering post, Meta said its HawkEye toolkit supports monitoring and debugging for recommendation and ranking models, including multiple model versions running as A/B experiments. (engineering.fb.com 1) (engineering.fb.com 2) The interview angle has shifted with that work. Google’s production machine learning material says teams must validate model quality, infrastructure compatibility, and reproducible training before serving a model, which pushes candidates beyond “Which algorithm would you choose?” and into “How would you keep this system working on Tuesday at 3 a.m.?” (developers.google.com 1) (developers.google.com 2) Recommendation systems make that especially visible because they are usually multi-stage systems that retrieve candidates first and rank them later. Meta’s December 2, 2024 post on Andromeda said its ads retrieval system narrows tens of millions of ad candidates to a few thousand before later ranking stages decide what to show. (engineering.fb.com) A/B testing sits at the end of that chain because offline scores do not settle whether a live system helps users or revenue. Uber wrote in a July 21, 2022 engineering post that building a reliable A/B testing platform at scale is “a massive challenge” because the data collection and experiment plumbing have to be correct before the result can be trusted. (uber.com) That is why prompts about serialization, containers, observability, and rollout strategy are showing up in prep lists with company names attached. They mirror the public engineering descriptions from Uber, Meta, and Google, where the hard part is often not training a model once, but operating it continuously after launch. (x.com) (uber.com) (developers.google.com)