For companies
Embeds with your customers and turns a model into shipped product. We find the ones who can actually do it, and we figure out the right way to bring them to your problem.
A forward-deployed engineer sits between your product and the people using it. They take a capable model and turn it into something a customer can rely on, working inside the customer’s problem rather than from a backlog.
The term came out of Palantir, where engineers were sent to live with the data and the users. With AI it matters more, because the gap between a demo that works once and a system a customer trusts is wide, and only closes when someone owns both ends.
Most hiring filters on credentials and years. The thing that makes a forward-deployed engineer good does not show up there. It shows up in how they work, which means you have to watch them work to see it.
That is what we do. We watch people work instead of reading resumes, so the person we send you is calibrated on the actual job, not the interview. Sometimes that is a hire. Sometimes it is a project or a person embedded for a while. We work out the shape with you.
They embed with a customer, scope the real problem, and ship a working system on top of a model. They own the result, not just the code.
A solutions engineer mostly supports a sale. A forward-deployed engineer builds and ships the product inside the customer’s problem, and stays on the hook for whether it works.
In the US, total compensation for strong AI-native forward-deployed engineers usually lands between $170k and $240k, plus equity at startups.