Most frontend interviews measure the wrong things. They over-index on algorithm trivia and under-index on the judgment that actually separates a strong engineer from an average one: how they structure components, reason about state, handle edge cases, and make accessible, performant interfaces under realistic constraints.
This guide lays out a framework you can reuse for every frontend role, then shows how to turn it into a scored assessment that predicts on-the-job performance.
Start from the work, not the syllabus
Before writing a single question, list the three to five things the person will actually do in their first 90 days. For most frontend roles that looks like building components from a spec, integrating an API, debugging a rendering issue, and making a UI accessible. Every question you write should trace back to one of those.
Rule of thumb
If you can't name the real task a question maps to, cut it. Trivia inflates difficulty without adding signal.
The four layers worth testing
- Fundamentals — semantic HTML, CSS layout, and the language features they'll use daily.
- Framework fluency — component structure, state, and data flow in your actual stack.
- Production judgment — accessibility, performance, and error handling under realistic constraints.
- Communication — can they explain a trade-off and justify a decision in writing?
Weight these by seniority. For a junior role, fundamentals and framework fluency carry most of the score. For a senior role, production judgment and communication should dominate — anyone can center a div by then.
Prefer applied tasks over recall
A short, applied task — 'here is a broken component, fix the re-render bug and explain what caused it' — tells you far more than a multiple-choice question about hook rules. Applied tasks are also harder to game with memorized answers and easier to score consistently against a rubric.
The best predictor of whether someone can do the job is watching them do a small, realistic slice of the job.
— A hiring principle worth tattooing on every scorecard
Score against a rubric, not a gut feeling
Define what a 1, 3, and 5 looks like for each dimension before candidates start. This is what makes results comparable across candidates and interviewers, and it's the single biggest lever for reducing bias in technical hiring.
With Vertana, you can encode this framework directly: mix multiple-choice fundamentals with live coding tasks, let AI draft role-specific questions, and score every submission against the same rubric so rankings reflect ability rather than interviewer mood.
Put this into practice