What Forward-Deployed Engineering Teaches You
Three and a half years as a forward-deployed engineer taught me more about building AI products than any amount of platform work could have.
When I told people my title was “Forward Deployed Software Engineer,” the usual response was a polite nod and a subject change. Fair enough. It sounds like a military posting. In a way, it is: you’re an engineer stationed at the customer, building product from inside their problem.
Now that OpenAI, Anthropic, and half the AI industry are hiring forward-deployed engineers to get agents into production, the model Palantir pioneered has become the way serious AI companies ship. Having spent three and a half years doing it at Gecko Robotics, across power plants, refineries, and paper mills, here’s what the job actually teaches you.
You can’t spec what you haven’t seen
Asset Manager, the product behind Gecko’s first SaaS deal, didn’t come from a product-requirements document. It came from watching how reliability engineers actually made capital decisions: spreadsheets, tribal knowledge, and PDFs of inspection reports. That was when it became clear the data from our robots could replace guesswork with evidence. The insight wasn’t available from headquarters. It was only visible standing next to the user.
That’s the core FDE trade: you give up the clean abstractions of platform work and get, in exchange, the truth.
Speed is a feature of proximity
When the person who feels the pain sits across the table, iteration cycles collapse from quarters to days. Ship something rough on Tuesday, watch it fail in an interesting way on Wednesday, ship the fix on Thursday. The feedback loop isn’t a dashboard metric. It’s a facial expression.
This is exactly why the FDE model is winning in AI. Agents are too new, and too dependent on messy organizational context, to be built from spec sheets. The teams shipping working agents are the ones embedded close enough to watch them fail in the wild.
The prototype is a promise
The FDE failure mode is the permanent hack: a demo that works for one customer and calcifies into unmaintainable glue. The discipline that separates forward-deployed engineering from consulting is knowing which prototypes are throwaway and which are the first draft of a product. Then comes the harder second job of generalizing. Cantilever AI started as work for specific power customers; it became a platform because we kept asking which parts of the solution were about this plant and which were about every plant.
Empathy compounds like technical skill
The most valuable thing I carry from those years isn’t a technology. It’s a reflex. Before writing code, I now instinctively ask: who touches this, what were they doing right before, what happens to them if it’s wrong? In an era where AI systems increasingly act on people’s behalf, that reflex might be the most transferable engineering skill there is.
Different industries ask different questions, but the posture holds up: sit close to the problem, ship, watch, generalize. Forward deployment isn’t a role. It’s a way of building.