A Decoder Ring for AI Job Titles

Summary

A Cheat Sheet for Job Titles in the AI Ecosystem Even when you live and breathe AI, the job titles can feel like a moving target. I can only imagine how mystifying they must be to everyone else. Because the field is actively evolving, the language we use keeps changing. Brand new titles appear overnight or, worse, one term means three different things at three different companies. This is my best attempt at a “Cheat Sheet for AI Titles.” I’ll try to keep it updated as the jargon shifts, settles, or fades away. As always, shoot me a note with any additions, updates, thoughts, or feedback. The AI Job Title Decoder Ring While collecting examples of titles from job listings, Twitter bios, and blogs, a pattern emerged: nearly all AI job titles are created by mixing-and-matching a handful of terms. Organizing the Post-Its on my wall, I was reminded of “mix-and-match” children’s books: If we swap out the dinosaur parts above with the adjectives and nouns from my collected examples, we get: Sliding these columns up and down, we can assemble most AI job titles. (Though I have yet to see some combinations, like, “Applied AI Ops”.) Let’s first break down the modifiers: Forward Deployed: People who work closely with customers, helping them develop new applications powered by their own company’s technologies. They learn their customer’s business, constraints, and goals, then translate that context directly into features, integrations, and working code. Applied: People who conceive, design, support, and/or build products and features powered by AI models. The key here is that they are applying AI to a domain problem; they are not helping build the AI itself. There is plenty of overlap here: most Forward Deployed workers are working on Applied problems. They usually aren’t training new models with the customer. The domain column is rather awkward, mostly for historical reasons. The terms “ML” and “Gen AI” are subsets of the broader “AI” domain. “Gen AI” as a term only arrived afte...