Hiring the Engineerportfolio signals and craft test

Hiring for the Experience Engineer archetype (per 10.3) requires evidence of downstream depth: production-fidelity craft, systems architecture, code-integrated interaction design, motion timing at the scale of shipped work. The same hiring logic applies, with the evidence in a different place. For the Engineer, the interview is built around watching craft happen at fidelity, where hearing it described tells you less.

Before any portfolio review, the background already says a lot: candidates who become Experience Engineers arrive with a background-and-aspiration signature different from the Strategist’s. Their recent work leans toward UI design, interaction design, animation, prototyping, and design systems work. Ask what work energises them and the answer is production craft: easing curves, component behaviour, token architecture, the feel of an interaction at actual runtime. Business outcomes and research decks never come up. Ask where they want to grow and the arrow points downstream: deeper into code, deeper into motion, deeper into systems thinking at the implementation level.

Again, the background-and-aspiration filter is a first-pass signal, a starting read and no substitute for deeper evaluation.

Portfolio signals at the leader-hiring scale:

• Shipped work at production fidelity: running products and live links. Static mockups alone are not enough. The evidence of craft at the downstream end lives in the artefact running in its actual medium.
• Systems work with governance evidence: token architectures with contribution models, component libraries with versioning, design-system documentation sites that show the candidate’s decisions shaping other designers’ work.
• Motion reels or interaction demonstrations at production quality, shown in the actual medium, where you can feel the timing and easing for yourself.
• Code-integrated artefacts where relevant: CodePen or GitHub presence, contribution to open-source design-tooling projects, prototypes that cross the design-code boundary. With AI producing working code in minutes, a live prototype on its own shows less than it used to. Ask for the iteration history behind it: commits, discarded versions, and the decisions between them.