Assessing the fastest-growing job is tricky. For example, using job-posting data isn’t great because there could be lots of duplicate postings out there or a lot of the postings could be going unfilled. Another big issue is defining exactly what a job is: The exact same responsibilities could be associated with the job title “data scientist”, “data engineer” or “ML engineer”, depending on the particular job titles a particular company decides to go with. So, whoever’s evaluating job growth is going to end up bucketing groups of related jobs and responsibilities into one particular, standardized job-title bucket, probably these days in a largely automated, data-driven way; if you dug into individual examples, I’m sure you’d find lots of job-title standardizations you disagreed with but some kind of standardization approach is essential to ensuring identical roles with slightly different job titles get counted as the same thing.
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AI Engineering 101, with Ed Donner
My holiday gift to you is my Nebula.io co-founder Ed Donner, one of the most brilliant, articulate people I know. In today's episode, Ed introduces the exciting, in-demand "A.I. Engineer" career — what's involved and how to become one.
After working daily alongside this world-class mind and exceptional communicator for nearly a decade, it is at long last my great pleasure to have the extraordinary Ed as my podcast guest. Ed:
• Is co-founder and CTO of Nebula, a platform that leverages generative and encoding A.I. models to source, understand, engage and manage talent.
• Previously, was co-founder and CEO of an A.I. startup called untapt that was acquired in 2020.
• Prior to becoming a tech entrepreneur, Ed had a 15-year stint leading technology teams on Wall Street, at the end of which he was a Managing Director at JPMorganChase, leading a team of 300 software engineers.
• He holds a Master’s in Physics from the University of Oxford.
Today’s episode will appeal most to hands-on practitioners, particularly those interested in becoming an A.I. Engineer or leveling up their command of A.I. Engineering skills.
In today’s episode, Ed details:
• What an A.I. Engineer (also known as an LLM Engineer) is.
• How the data indicate A.I. Engineers are in as much demand today as Data Scientists.
• What an A.I. Engineer actually does, day to day.
• How A.I. Engineers decide which LLMs to work with for a given task, including considerations like open- vs closed-source, what model size to select and what leaderboards to follow.
• Tools for efficiently training and deploying LLMs.
• LLM-related techniques including RAG and Agentic A.I.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.