Moonhub

Automatically identifies and surfaces qualified passive candidates from global networks and talent pools who match specific technical role requirements. Uses AI algorithms to proactively find candidates beyond those applying directly to job postings.

Automatically evaluates candidate profiles and resumes against job requirements using machine learning to identify qualified prospects. Filters candidates based on technical skills, experience level, and role fit without human intervention.

Provides human expert review of AI-screened candidates to validate algorithmic matches and reduce false positives. Expert recruiters assess candidates for deeper fit, communication ability, and other factors that algorithms may miss.

Ranks candidates based on algorithmic fit scores that compare their qualifications against job requirements. Produces ranked lists showing best matches first to prioritize recruiting efforts.

Eliminates manual resume review and initial qualification activities by automating screening and sourcing workflows. Reduces recruiting team workload by 15-20 hours per open role.

Evaluates candidates' technical skills and experience against specific job requirements. Assesses programming languages, frameworks, tools, and technical competencies needed for engineering roles.

Provides access to Moonhub's curated global network of technical talent across multiple geographies and markets. Enables sourcing from international candidate pools beyond local job boards.

Organizes and tracks candidates through the recruiting workflow from sourcing through vetting to interview readiness. Provides visibility into pipeline status and candidate progression.