Nearly 4,000 workers were let go by Meta this week in yet another wave of layoffs, including a number of people who say they had good performance assessments the previous year. The impacted employees are confused and frustrated by the layoffs, which are a part of the company’s recent restructuring efforts.
Several workers said that they were included in the layoffs even though they had mid-year ratings of “At or Above Expectations” in 2024, according to a Business Insider article. These workers, who asked not to be named, said that they were in danger of being fired when their scores were suddenly dropped to “Meets Most” in the year-end reports.
Previously, Meta has advised managers to fire about 5% of the company’s lowest-performing employees. Internal records, however, revealed that the instruction permitted managers to lay off just lower-rated staff in order to include higher-rated people if they were unable to fulfill their targets. Many staff were caught off guard by this knowledge, which was not disclosed to the larger group of workers.
“When I received the email I was surprised by it mostly because I have a very solid performance history and no indicators of the last six months of performance problems,” one affected employee told BI. Another employee echoed this sentiment, saying they had been given no indication from their manager that their job was in jeopardy because the worker got good performance feedback.
Although Meta’s 2024 evaluation process started in December, many employees have not yet received their final assessments. Some employees were suddenly degraded, making them eligible for the cutbacks, despite having received positive feedback over the years. One employee was upset that after years of fulfilling standards, they were demoted during this most recent review cycle without providing an explanation.
As the firm concentrates on its investments in virtual reality and artificial intelligence, CEO Mark Zuckerberg is attempting to reduce Meta’s personnel through the layoffs. Meta intends to keep reducing what it views as its worst performers even as it seeks to add additional machine learning engineers.
