Forward-Deployed Engineering

I was on a call with a Fortune 100 CMO recently who asked why he was having a tingling sensation that all his martech was about to become obsolete. He'd spent tens of millions on a stack that suddenly felt like it was built for a different era. He's not wrong.

The consulting model doesn't help here. A team flies in, conducts interviews, produces a strategy deck, and flies out. Six months later you have a PDF and no running code. The agency model isn't much better—campaigns ship, but capabilities don't compound. And SaaS sells you someone else's mirror when you need your own mold.

The thing about AI is that the value isn't in the plan. It's in the iteration. Every prompt refined, every edge case handled, every pipeline tuned makes the system meaningfully smarter. That learning can't happen from the outside.

We wrote about this in more detail, but the short version: an FDE embeds with your marketing org. Attends your standups. Gets access to your systems. Builds in your environment. Gone are the days of four layers of suits sitting between engineers and customers.

1. 1

   Embed & Orient

   First week: understand your data, tools, team, and challenges. No questionnaires—direct observation.

2. 2

   Ship a Working System

   Within days, ship a working prototype. Not a mockup. Running code with your real data.

3. 3

   Iterate with Your Team

   Your team uses it, surfaces edge cases. Your FDE iterates daily—the system gets smarter every cycle.

4. 4

   Transfer Ownership

   When it's production-ready: docs, training, full ownership transfer. No lock-in.

Honestly, the list changes with every client. But the things that keep coming up: AI agents that actually encode your brand voice (not a chatbot with your logo slapped on), content systems that leverage your private tokens to produce on-strategy output, data infrastructure that connects your marketing stack to AI capabilities. Sometimes it's an evaluation framework so you can actually measure whether the AI is doing a good job by your standards.

The common thread is that none of these are things you can buy off the shelf. They require your data, your context, your people in the loop. That's the whole point.

Palantir's Shyam Sankar has this great framing: there are engineers who know how to build the right thing (MacGyver types— their high is “I solved the problem”) and engineers who know how to build it the right way (artists— “don't you see how beautiful my architecture is?”). Our FDEs skew MacGyver. They care about whether it works, not whether it's elegant.

But that undersells it. These aren't fresh grads who happen to know Python. Our FDEs have built martech companies, run engineering orgs, worked inside the kinds of brands they now serve. They can sit in a marketing strategy meeting at 10am and have a working prototype by the afternoon—not because they're fast coders, but because they already understand the problem domain. That combination is genuinely rare, and it's the thing that makes the model work. Without it you're just staff aug.

The other thing that matters: they build to hand off. The goal is always for your team to run it without us. Clean code, docs that actually make sense, training that sticks. We come back when you have new problems, not because the old ones broke.

Interested in the role? We're hiring FDEs.