How we hire applied ML engineers

No leetcode. One system-design interview, one paper-reading interview, one project debrief. We tell candidates what we are looking for in advance.

Production AI work has a way of punishing abstractions. The useful lesson usually appears after a model has met a real workflow, a real constraint, and a stakeholder who can say precisely what would make the system unsafe.

At Kryse we write these notes as field documentation: what we saw, what we measured, what failed, and what pattern we would reuse. The goal is not novelty. The goal is a system another senior engineer could operate without theatrical confidence.

The durable pattern is simple: define the failure modes, turn them into evals, wire those evals into the release path, and make the human handoff explicit before the model does anything expensive.