Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical proficiency
35% weight
Check depth in semantic HTML, modern CSS (grid, container queries, cascade layers) and a framework like React or Vue: ask about state handling, hooks, and component APIs they built.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Explains component composition, CSS specificity and re-render behaviour precisely, and names build tooling such as Vite, Tailwind or Storybook they configured.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe how they weigh bundle size, hydration cost and browser support: ask about Core Web Vitals numbers, code splitting decisions, and when they rejected a UI library.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Cites measured LCP or CLS improvements, justifies framework and styling trade-offs, and knows where a design system token layer belongs.
03
Evaluation factor
Evidence and rigour
25% weight
Test verification habits: Lighthouse and axe audits, WCAG 2.2 AA fixes, keyboard and screen reader passes, visual regression snapshots, and cross-browser checks on real devices.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Describes specific accessibility defects found and remediated, plus test coverage using Playwright, Jest or Chromatic rather than manual clicking alone.
04
Evaluation factor
Collaboration and communication
15% weight
Assess handoff with designers and backend: Figma spec interrogation, pushing back on unbuildable states, agreeing API shapes, and documenting components for other developers.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Gives concrete examples of resolving design ambiguity, contributing to shared component docs, and reviewing peers' front-end pull requests constructively.
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