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Joe Isaacson

Joe Isaacson

Engineering Manager, Machine Learning at Facebook

 LinkedIn profile


10 Years



Management Experience

Coaching / Training / Mentorship
Data team
Scaling Team

Hey everyone, I'm Joe. I lead an organization of machine learners at Facebook, focused on the News Ecosystem. Previously, I have lead engineering teams spanning biological, cell engineering (Asimov), recommendation systems (Quora), Search/IR and Ad tech (URX; YC-S13). Outside of work I enjoy running, hiking and writing. I look forward to working with you all on Plato!

Joe is not available in the next 4 weeks.


Shaping Metric Improvements
16 February

Joe Isaacson talks about processes to follow to ensure the setting of appropriate metrics for your team.

Team processes
Joe Isaacson

Joe Isaacson

Engineering Manager, Machine Learning at Facebook


VP Engineering


Jan 2018 - current

Asimov aims to radically advance humanity's ability to design living systems. We strive to enable biotechnologies with global benefit by combining synthetic biology and computer science. We're hiring! Please reach out or go to asimov.io if you'd like to join our growing software team. We're hiring individuals with strong backgrounds in software infrastructure and platform development, large scale data warehousing, software and hardware robotic automation, machine learning of biological systems, and bioinformatics / data science.

Senior Engineering Manager


Oct 2016 - Dec 2017

I managed teams of 4-20 engineers as a leader in the machine learning organization. The ML organization is a cross functional group, pairing with all of Quora's product teams to improve reader, writer and advertiser experiences through development of scalable, predictive software systems. We delivered significant improvements on: Core metrics tracking user engagement via improved ranking algorithms and ML serving systems powering Quora's Home feed, Email digests, Topic pages, and Ask to Answer features. Revenue via improved advertising targeting and optimization. We carefully measured and modeled simultaneous advertiser return with user experience, leading to improved auction efficiency, targeting and placements with minimal disruption to readers and writers. As well, I enjoyed working with the whole engineering organization to design and launch novel products, refactor and redesign legacy infrastructures, rethink engineering culture and push for high quality software and strong cross-team collaborations.

Advisor, ML and Engineering Managmenet


Sep 2016 - current

I am an advisor in machine learning engineering and engineering leadership. Currently I volunteer on the Plato platform and on Leap.ai as a mentor to new managers. Previously, I was an advisor at Carlypso (Y-Combinator acquired by Carvana), and several early stage, stealth startups working on ML strategy and product.

Head of Science

URX (acquired by Pinterest)

Jan 2014 - Sep 2016

URX was the world's first deeplink search engine, enabling developers to monetize applications via contextually relevant actions. I lead the data science team, starting as an IC and growing to manage a group of engineers and scientists. In these roles I helped to build our search recommendation system and knowledge graph, LTV calculations for ad targeting, machine learning to optimize web crawling (deduplications, freshness and prioritization) and our A/B testing engine. As well, I developed hiring pipelines for machine learners, tripled the size of the team and presented at local meetups and small conferences. During our acquisition, I helped lead due diligence for our ML systems. And at Pinterest, I lead a small project to reinvent search and discovery. I was a member of the infrastructure team and built a spark pipeline for compiling search indices. This work has since been expanded and is described in https://medium.com/@Pinterest\_Engineering/manas-a-high-performing-customized-search-system-cf189f6ca40f

Data Engineer

MIT Lincoln Laboratory

Aug 2011 - Dec 2013

I leveraged applied mathematics and software engineering to solve technical problems arising in today’s chemical and biological defense community. Projects included: A forensic bioinformatics pipeline to map/reduce DNA to feature sets and develop ML algorithms to predict physical traits, ancestry and deconvolve mixtures. Several papers have since been published on this platform including https://www.biorxiv.org/content/biorxiv/early/2017/11/27/225805.full.pdf Mass Spec signal processing algorithms to improved detection performance of standard sensors. Computer vision projects including algorithms to identify and track individuals across cameras with significant occlusion, methods to summarize events across long video sequences, and projects in SfM to reconstruct 3D scenes from synchronized cameras

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