The Problem
A growing startup needs to fill 6 engineering positions in 8 weeks. The HR manager writes each job description from scratch, manually reviews 200+ resumes per role, and drafts individual offer letters with legal review for each hire. The process takes 12-15 hours per role just in administrative work. Inconsistent job descriptions attract the wrong candidates, resume screening is subjective depending on who reviews them, and offer letters take 3 days to produce because they shuttle between HR, legal, and the hiring manager for approval.
The Solution
Using job-description to generate consistent, compelling postings, applicant-screening to score and rank candidates against role requirements, resume-tailor to analyze how well each resume maps to the role, and offer-letter to produce standardized offers with the correct compensation and legal terms, the startup compresses weeks of admin work into hours.
Step-by-Step Walkthrough
1. Generate standardized job descriptions
Create job postings that accurately describe each role's requirements, responsibilities, and compensation range.
Use job-description to create a posting for Senior Backend Engineer. Requirements: 5+ years Python experience, PostgreSQL, distributed systems, AWS. The role reports to the VP of Engineering. Compensation: $165,000-$195,000 base plus 0.1-0.15% equity. Include our engineering culture values: code review rigor, on-call rotation, and 20% time for technical exploration. Output both a full JD for our careers page and a shortened version for LinkedIn.
2. Screen and rank incoming applications
Process the applicant pool against weighted criteria to surface the strongest candidates.
Use applicant-screening to evaluate 214 applications for the Senior Backend Engineer role. Score each resume against these weighted criteria: Python experience (25%), distributed systems (20%), relevant industry experience (15%), education (10%), open source contributions (10%), communication signals from cover letter (10%), and culture fit indicators (10%). Flag candidates who meet all hard requirements and rank them by overall score. Output the top 30 candidates to /hiring/backend-senior/shortlist.csv.
The screening tool produces a ranked summary with scores broken down by criterion:
Applicant Screening — Senior Backend Engineer
==============================================
Applications received: 214
Hard requirements met: 68 (31.8%)
Shortlisted (top 30): 30
Rank Name Score Python DistSys Industry Edu OSS Comms Culture
1 Sarah Chen 94.2 25/25 20/20 13/15 8/10 10/10 9/10 9.2/10
2 Marcus Rivera 91.8 25/25 18/20 14/15 9/10 8/10 9/10 8.8/10
3 Aisha Patel 89.5 23/25 19/20 12/15 10/10 9/10 8/10 8.5/10
4 David Kim 87.3 24/25 17/20 13/15 7/10 10/10 8/10 8.3/10
5 Elena Volkov 85.9 22/25 20/20 11/15 8/10 7/10 9/10 8.9/10
...
30 James Okonkwo 72.1 20/25 14/20 10/15 8/10 6/10 7/10 7.1/10
Rejected (hard requirements not met): 146
- Insufficient Python experience (<3 years): 89
- No distributed systems background: 41
- Missing cover letter: 16
Output: /hiring/backend-senior/shortlist.csv
The rejection breakdown helps the recruiter understand whether the job posting is attracting the right applicant pool or needs adjustments to its requirements or distribution channels.
3. Analyze resume-to-role fit for shortlisted candidates
Deeply compare each shortlisted candidate's experience against the specific role requirements.
Use resume-tailor to analyze the top 30 shortlisted resumes against the Senior Backend Engineer JD. For each candidate, produce a fit report: skills matched, skills missing, years of relevant experience, notable achievements, and a recommended interview focus area (the skill gap that the interview should validate). Flag any candidates whose resumes suggest they are overqualified or underqualified by more than 2 years of experience.
4. Generate offer letters for selected candidates
Produce legally compliant offer letters with correct compensation, equity terms, and start dates.
Use offer-letter to generate an offer for candidate Sarah Chen. Base salary: $185,000. Equity: 0.12% over 4-year vest with 1-year cliff. Signing bonus: $15,000. Start date: March 17, 2026. Include standard clauses: at-will employment, confidentiality agreement, IP assignment, 90-day probation period, and benefits enrollment eligibility. Generate both PDF and DocuSign-ready formats. CC the VP of Engineering and legal counsel.
Real-World Example
The startup ran this pipeline for all 6 engineering roles. Job descriptions were generated in 30 minutes total instead of 3 days. The screening tool processed 1,180 total applications across all roles and shortlisted 142 candidates in under 2 hours, compared to the 80+ hours it would have taken to review manually. The HR manager reported that interview-to-offer conversion improved from 1-in-5 to 1-in-3 because better screening meant interviewers spent time with genuinely qualified candidates. All 6 positions were filled in 6 weeks instead of the expected 8, and offer letters were delivered same-day instead of the previous 3-day turnaround.
Tips
- Calibrate screening weights by having the hiring manager manually score 10 resumes first, then adjust the automated weights until the tool's ranking matches the manager's intuition.
- Keep the hard requirement threshold strict but the scoring flexible. A candidate with 4 years of Python but exceptional distributed systems experience should still surface above someone with exactly 5 years of Python and nothing else.
- Generate both a full-length job description for the careers page and a condensed version for job boards. LinkedIn posts with descriptions under 300 words receive significantly more applications than longer ones.