December 16

Ep. 17 Jake Mannix – Self-Professed Math Nerd Physicist turned AI Engineer

Artificial Intelligence, Machine Learning, Open Source, Software Engineering

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Biography

Jake Mannix is currently the Data Architect of Search Relevance at Salesforce. In other words, he’s an AI Engineer. Jake lives in the intersection of search, recommender-systems, and applied machine learning, with an eye for horizontal scalability and distributed systems. Prior to his current position, Jake was the Lead Data Engineer in the Office of the CTO at Lucidworks.

In college, Jake studied algebraic topology and particle cosmology. He got his PhD at the University of Washington before transitioning into technology.

Some of Jakes previous accomplishments

  • Built out LinkedIn’s search engine
  • At Twitter, built user/account search system and lead that team before creating the Personalization and Interest Modeling team.
  • Apache Mahout committer, PMC Member (and former PMC Chair).

For other tech roles and descriptions click here.

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Episode Summary

Jake is a self-professed math and physics nerd. He has worked for a number of large and small tech companies but has been primarily focused on a career in search and artificial intelligence. Jake is an AI Engineer. For Jake, “it’s all about taking a big messy complex system” and then “boiling it down to 2 variables or 3 variable that interact in a way that replicates the key aspect of the complexity.”

Jake began his career in academia, but found himself in an endless frenzy where the job market was “100 people applying for 1 job.” This is when he got into tech.

To this day, Jake has taken only one programming class – Intro to Programming in C. He also takes pride in being a “whiteboard guy”, having written a 100 page senior thesis in Physics with “not a single decimal point!”

In this episode we’ll also cover: 

  1. The process of transitioning into programming
  2. How math was never the scary part, but programming was.
  3. What an AI Engineer does in the day to day
  4. Advice for potential data engineers, relevance architect, etc.

Key Milestones

[01:55] – Jake’s backstory
[05:32]Transitioning into coding for a living
[07:14] – Breaking into Search and AI
[09:18] – hard core math vs engineering
[14:39] – What is the day to day of an architect of search relevance
[18:51] – Thoughts about the future of data engineering
[21:55] – What are the challenges of the role
[26:00] – The challenges and biases of ethics in AI
[30:36] – Advice for aspiring technologists

Key Quotes

“This abstraction layer is the heart of both being a mathematician and physicist.” 

“probably 10 percent or less is spent doing anything really math related”

“If I’m going to be able to settle down and have a family, live in one place, and actually develop a career with some stability, I’m going to need to try something where the job market is not 100 people applying for one job.”

“It’s all about taking a big messy complex system, boil it down to 2 variables or 3 variable that interact in a way that replicates the key aspect of the complexity.”

“In an architect type position, your job is to know a bunch of stuff and help make sure that other people don’t waste their time doing things when theres a way to do it easier.”

“When you’re in an architect type position/level your job is to know a bunch of stuff and to help make sure other people don’t waste their time” [16:48]

“Data engineers need to think about both the data warehouse, data at rest, as well as how it moves at a run time.”

“I see a future where you treat AI libraries as if they were things that you should able to kind of inject into your system wherever you think you can make use of it.”

Additional Resources

TF/IDF – Statistic for how important a word is in a document

CONNECT WITH JAKE MANNIX
LinkedIn
Twitter

CONNECT WITH GRANT INGERSOLL
LinkedIn
Twitter


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