Intermediate

AI-Powered Candidate Screening

Leverage AI to automatically screen resumes, rank candidates, and identify the best matches for your roles. Save hours of manual review while improving hiring quality.

10 min read

Kumo AI Team

AI Screening Capabilities

Resume Parsing

Automatically extract skills, experience, and education from resumes in any format

Candidate Ranking

AI scores candidates based on job requirements and historical hiring data

Automated Filtering

Filter candidates by location, experience level, salary expectations, and more

Skill Matching

Match candidate skills to job requirements with confidence scores

Setting Up AI Screening

1. Define Job Requirements

Start by clearly defining what you're looking for in candidates:

• Must-have skills: Essential technical and soft skills
• Experience level: Minimum years of relevant experience
• Education requirements: Degree requirements or equivalent experience
• Location preferences: On-site, remote, or hybrid arrangements

Pro Tip

The more specific your requirements, the better the AI can match candidates. Use concrete skills and technologies rather than vague descriptions.

2. Configure Screening Criteria

Experience Level

Years of relevant experience in the field

Entry (0-2 years)Mid (3-5 years)Senior (5+ years)

Technical Skills

Programming languages, frameworks, and tools

Required skillsPreferred skillsNice-to-have skills

Education

Relevant degrees, certifications, and training

Degree requiredRelevant fieldEquivalent experience

Location & Availability

Geographic location and start date availability

Local candidatesRemote candidatesRelocation willing

3. Set Scoring Weights

Customize how the AI prioritizes different factors:

Example Scoring Configuration:

Technical Skills Match

40%

Experience Level

30%

Education & Certifications

20%

Location & Availability

10%

4. Test and Refine

Before going live, test your screening configuration:

• Upload sample resumes to test the scoring
• Review AI rankings against your manual assessments
• Adjust weights based on results
• Set minimum score thresholds for automatic filtering

Understanding AI Candidate Scores

Score Breakdown

90-100 Points

Excellent Match - Priority Review

75-89 Points

Good Match - Consider for Interview

60-74 Points

Partial Match - Review if Pool is Small

Below 60 Points

Poor Match - Auto-reject or Archive

AI Screening Best Practices

Do's

Regularly update job requirements based on market changes

Review AI decisions to improve accuracy over time

Use AI as a first filter, not final decision maker

Set reasonable score thresholds to avoid missing good candidates

Avoid

Over-relying on AI without human oversight

Setting unrealistic or biased screening criteria

Ignoring diverse candidate backgrounds and experiences

Never reviewing or updating screening parameters

Next Steps

Once you've configured AI screening, optimize your interview process:

Smart Interview Scheduling

Automate interview coordination for top candidates.

Pipeline Management

Organize and track candidates through your hiring stages.