For companies
Designs evals and RL pipelines so quality is measured, not guessed. We find the ones who can actually do it, and we figure out the right way to bring them to your problem.
An eval engineer makes model quality measurable. They build the harnesses, datasets, and scoring that turn a vague sense of "this feels better" into a number you can move on purpose.
On the RL side, they design the data and reward pipelines that improve a model. Without good evals, RL is guessing with extra steps, so the two jobs sit close together.
Most hiring filters on credentials and years. The thing that makes a eval & rl 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.
No eval & rl engineer roles are posted right now. Tell us what you need at [email protected] and we will start.
They build the evals that measure model quality, so teams can tell whether a change made things better or worse instead of guessing.
Because most AI teams ship faster than they can measure. An eval engineer is what turns shipping into improving.
In the US, total compensation usually lands between $190k and $280k, higher at frontier labs.