The Rise of the Forward Deployed Engineer — and How To Do the Job Right

Engineers who are “forward deployed” (FDEs) have become one of the most sought‑after roles in the AI sector, as labs, startups and investment funds bring them in to work directly on their clients’ operations and solve specific problems. Voono Ganesh, CEO of Kepler, recounts his experience building FDE functions at three different organizations: Palantir, where he led the “Project Frontline” program that trained about 250 engineers who now work at companies such as OpenAI and Anthropic; Citadel, where the goal was to provide data and software tools that generate alpha for portfolio managers; and Kepler, where the role sits inside the product and the priority is to avoid incorrect answers, because a wrong response is worse than none.

Ganesh notes that, despite the title’s popularity, there is no single definition of the position. In a recent a16z‑sponsored meeting, participants from Snowflake, Anthropic and other startups discovered that “forward deployed” can refer to sales engineers who attend the second call, to quota‑bearing reps who code in Python, or even to consultants who deliver solutions when the internal product cannot. This lack of consensus creates confusion about the responsibilities and success metrics of FDE teams.

Understanding what the role truly entails is essential because it shapes how organizations structure their AI teams, allocate resources, and set delivery expectations, which in turn affects the speed and quality of technology adoption by clients.

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