AI labs want Slack DMs, emails, and other internal records to train business-focused models.
Anthropic is investing $100 million in a training initiative for Frontier Deployed Engineers, aiming to help companies deploy Claude and build AI skills at scale.
Anthropic announced a $100 million training initiative for Frontier Deployed Engineers, or FDEs, a role focused on helping companies put AI systems to work. The company says it hopes to train 10,000 people through the program by the end of 2027. The effort is designed to expand corporate use of Anthropic’s Claude tools while addressing demand for engineers who can translate AI capabilities into new workflows.
The initiative is described as a quasi-fellowship for engineers from other companies. Participants attend four-day on-site intensives, then complete 12-week residencies using Anthropic’s Claude tools at their own organizations. Companies nominate engineers, Anthropic interviews candidates, and accepted participants work in a simulated agentic environment before taking a practical test and continuing with Anthropic mentors.
Anthropic executives identified several traits that define strong FDE candidates: familiarity with agentic AI systems, problem-solving ability, communication skills, and experience as skilled engineers. Steve Corfield, Anthropic’s head of global business development and partnerships, added curiosity as another key attribute. Communication stands out because these engineers are expected to help organizations rethink workflows and navigate the human side of AI adoption.
Business Insider reports that FDE job postings have boomed as more companies buy AI software and need help deploying it. Anthropic calls its version of the role “Frontier Deployed Engineers,” while the broader title “forward-deployed engineer” was popularized by Palantir. Early cohorts have begun in New York and San Francisco, including engineers from Accenture, Bain, Novo Nordisk, and other organizations.
AI labs want Slack DMs, emails, and other internal records to train business-focused models.
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