Snorkel AI cuts 13% of workforce, targeting legacy roles amid shift to data-as-a-service model, avoiding AI-focused teams.

Snorkel AI, a Silicon Valley-based startup valued at $1.3 billion, laid off approximately 13% of its workforce, cutting 31 employees from its 240-person team. The company confirmed the move to Business Insider, attributing the restructuring to a strategic shift toward its data-as-a-service business model, which has led to the deprioritization of certain legacy operations. The layoffs, which took place on Wednesday, primarily impacted the software engineering team, with 13 employees let go. Notably, the company did not eliminate any roles in applied AI engineering or research science. Among the 25 employees with "AI" in their job titles, only three were affected, indicating that core AI development teams remained intact. Senior-level staff were also impacted, including the global head of business development and the director of AI solutions engineering. Snorkel AI said it is grateful for the contributions of those leaving and is providing support during the transition. The company emphasized that the changes are designed to focus resources on areas with the greatest potential impact and better align with evolving customer needs. Founded in 2020 and spun out of Stanford, Snorkel AI specializes in connecting AI companies with human experts to improve data quality for training models. It positions itself as a solution to the costly and time-consuming process of data labeling, a critical component in advancing generative AI. The company recently raised $100 million in a Series D round, bringing its valuation to $1.3 billion, and counts major tech firms like Google and Anthropic among its clients. The layoffs come amid broader industry turbulence. Snorkel AI joins Scale AI, another major player in the data labeling space, which laid off 14% of its workforce and 500 contractors in July. Scale AI’s restructuring followed Meta’s acquisition of a 49% stake and the hiring of its CEO, Alexandr Wang. Scale AI cited overhiring, market pressures, and unprofitability as key reasons for the cuts, and also reported terminating a team focused on AI safety testing. As AI companies continue to refine their models, demand for high-quality training data remains strong. However, the sector is facing growing scrutiny over costs, sustainability, and workforce stability, with multiple startups adjusting their strategies to remain competitive.