The honest fit test

FDE work rewards a specific temperament, and it's worth being honest about whether it's yours before you chase it for the salary alone (and the salary is real: average total comp is now reportedly around $238K, with a range from roughly $205K to $486K, and senior FDEs at frontier labs going well past that).

In the last post, we built up the idea of a Forward Deployed Engineer (FDE) from first principles: an engineer who sits with the customer and ships the code, collapsing the usual gap between "understanding the problem" and "building the solution." That's the theory.

If you've browsed tech job boards recently, you've probably noticed a title popping up everywhere: Forward Deployed Engineer. Palantir, OpenAI, Anthropic, Google, Databricks — all hiring for it, some by the dozens.

When an AI project fails, the post-mortem almost always lands on the model — the wrong architecture, insufficient training data, a tuning choice that didn't generalise. Rarely does the conversation turn upstream, to the input layer where data was collected, cleaned, and structured before it ever reached the model.

Every enterprise AI pilot looks good. This is not a cynical observation — it is a structural reality of how pilots are designed. The data is curated. The scope is narrow. The use case is selected precisely because it is the one where the existing data is cleanest and the model is most likely to perform well.

There is a conversation happening in every enterprise AI post-mortem that nobody wants to be the first to say out loud: the model wasn't the problem. The model did exactly what it was trained to do.