The Forward Deployed Engineer
The book. The deployment playbook, the discovery process, the failure modes, and the organisational politics nobody writes down.
Read the early releaseI make enterprise software actually land, and I write down how, because when I started doing this work there was nothing to read.
A 90-day program with personal reviews and live practice for your move into FDE or Applied AI.
How you run discovery when the stakeholder is hostile. How you scope something you have never seen before. When to build the throwaway version, and when to delete it. That craft gets passed around verbally between people who have done it, and almost never written down.
So I wrote it down: what the role actually is, how it differs from solutions engineering and consulting, what it pays, and how to get in.
Read: what a forward deployed engineer actually isThe book. The deployment playbook, the discovery process, the failure modes, and the organisational politics nobody writes down.
Read the early releaseSeveral have secured them, more are in interview loops, and everyone is levelling up together. A new roadmap every 30 days so you are never stuck. You are the priority here, not an audience.
JoinTalks on forward deployed engineering, applied AI in the enterprise, and what a deployment really costs before anyone writes a line of code.
EnquireMostly engineers, solutions teams and the people who hire them. If you are considering a talk, a sponsorship or a collaboration, that is the shape of the room.
I’m Paden, a Forward Deployed Engineer at Notion. My job is to make AI work inside someone else’s company: their data, their systems, their security review, their deadline. I’m writing the book on it.
The Forward Deployed Engineer (available now in early release on O’Reilly) is a handbook for the role, written from inside it while the role is still being defined. It covers technical discovery for customers who can’t write their own spec, data migration and legacy reality, building and grounding agents in customer systems, and evaluation a customer will actually believe.
Before Notion I was at Google, working on the identity infrastructure behind TLS and PKI, and at Bloomberg, where I built trading systems and re-architected the data pipeline that fed BloombergGPT. I’ve also worked on RLHF and evaluation pipelines at Scale AI and started my tech career as a technical architect at Accenture.
The path here wasn’t a CS degree. I trained as a musician at Juilliard, then studied information systems at Baruch. That mix of performance, consulting, and engineering is most of why the FDE role fit.
I also teach: live courses on the O’Reilly platform, cohorts and lightning lessons on Maven. And I write about all of it for 23,000+ engineers on LinkedIn.
Views here are my own, not my employer’s.
Speaking enquiries, deployment questions, or a note about the book. All welcome.