Meta layoffs hit an entire ML research team focused on infrastructure.

Sharon Goldman@sharongoldman

Photo: Meta

Image Credit: Meta

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.

---

After Wednesday’s Meta layoffs, which cut 11,000 employees, CEO Mark Zuckerberg publicly shared a message to Meta employees that signaled, to some, that those working in artificial intelligence (AI) and machine learning (ML) might be spared the brunt of the cuts.

“We’ve shifted more of our resources onto a smaller number of high priority growth areas — like our AI discovery engine, our ads and business platforms, and our long-term vision for the metaverse,” Zuckerberg wrote.

MetaBeat_Activating in the metaverse at a time when every dollar counts_Trim

However, a Meta research scientist who was laid off tweeted that he and the entire research organization called “Probability,” which focused on applying machine learning across the infrastructure stack, was cut.

The team had 50 members, not including managers, the research scientist, Thomas Ahle, said, tweeting: “19 people doing Bayesian Modeling, 9 people doing Ranking and Recommendations, 5 people doing ML Efficiency, 17 people doing AI for Chip Design and Compilers. Plus managers and such.”

Another member of the team, a senior software engineer named Emily McMilin, responded by tweeting, “It took me almost 7 years at Meta to find a team as amazing as Probability.”

In his letter to employees (which came just a few weeks after Meta shares plummeted after its Q3 earnings call) Zuckerberg did note that he is currently in the middle of a “thorough review” of infrastructure spending.

“As we build our AI infrastructure, we’re focused on becoming even more efficient with our capacity,” he wrote. “Our infrastructure will continue to be an important advantage for Meta, and I believe we can achieve this while spending less.”

According to Meta’s Probability web page, the team makes “it radically easier for engineers to adopt machine learning techniques by deeply integrating machine learning into Facebook’s programming languages, developer tooling, and infrastructure.”

Commenting on life after the Meta layoffs, Ahle added that after a year and a half on the Meta team, “I hope to stay in the Bay Area a while longer, if anyone needs some algorithms.”