EvalcueAITechnical hiring
Technical interview scorecards and human review
Build structured interview scorecards that make candidate evidence easier to compare while keeping hiring decisions with human reviewers.
Use a small competency set
Scorecards work best when each criterion is meaningful. Typical technical criteria include correctness, problem decomposition, debugging, system reasoning, communication, and role-specific depth.
Require evidence for ratings
A score without evidence is hard to audit. Ask reviewers to reference the candidate’s answer, code, design choice, or follow-up response that supports the rating.
Keep AI and human judgment distinct
Automated analysis can summarize evidence and identify follow-up areas. Human reviewers should remain responsible for interpreting that evidence in the context of the role and for making employment decisions.
Compare consistently
Use the same assessment version and rubric when possible. If a role changes materially, create a new version instead of mutating the original and mixing candidates across different evaluation standards.