Hire software and AI engineers through mutual matching.

Start with what the person will actually do.

Import a job description or describe the role. Review the structured result before saving. Define what success looks like, which competencies matter, and how you would recognize strong evidence for each one.

For an AI engineering role, distinguish model experimentation from production ownership. For a full-stack role, describe the product and delivery responsibilities rather than listing every framework your team has touched.

Separate essential requirements from preferences.

Confirm working arrangements, employment type, compensation information, and other genuine constraints. Publishing requires the role to pass readiness checks. Missing information and high-severity compliance issues must be addressed.

A requirement that blocks candidates deserves scrutiny. Ask whether it is necessary for the work or simply familiar from a previous job description. The matching methodology explains how these requirements affect eligibility.

• Understand eligibility and scoring →
• Download the role scorecard worksheet →

Use match reasons to prepare better questions.

Review relevant experience, evidence context, and uncertainty. A score is not an independently verified credential or a prediction of job performance. Use it to decide what to investigate in a conversation.

Introductions require mutual interest. Participating candidate supply determines the available matches; Kif does not promise an instant shortlist or a hiring deadline.