AI/ML Engineer Positioning Guide

Your resume reads like a research paper. Product companies are filtering you out before a human sees it.

I have reviewed thousands of ML resumes across Google, Microsoft, and Airbnb. The pattern never changes. Strong engineers. Weak translation. You built real systems. Your resume describes models.

This guide gives you the exact framework hiring committees respond to, built from what actually clears a calibration room, not generic resume advice.

What you get:

— The Translation Framework: 5 patterns that convert research language into product language

— The Vocabulary Swap: 24 side-by-side rewrites, what to stop writing and what to write instead

— The Positioning Narrative Formula: the 3-sentence story that gets you forwarded to the hiring manager

— 3 full resume rewrite walkthroughs, fully annotated

— The FAANG ML Interview Signal: 5 things hiring committees evaluate that most ML candidates never prepare for

— A 30-day activation plan, week by week, start to submission

Built for product ML: recommendations, search and ranking, fraud and risk, NLP, forecasting. Not academic research.

This is Step 2 of the Data & AI Engineer Track.

Works standalone or as part of the full system.

Your move.