If you've ever wondered how an ATS turns a pile of PDFs into a ranked shortlist, it's a neat pipeline of boring-but-interesting problems.
The typical pipeline
Ingest: PDFs, Word files, and plain text arrive in inconsistent formats.
Parse: extract name, contact info, skills, and employment history into structured fields.
Normalize: map "JS," "JavaScript," and "ECMAScript" to one skill; standardize job titles and dates.
Score: compare structured data against the role's requirements.
Rank and explain: surface the shortlist along with why each candidate matched.
A simplified scoring example
python
def score_candidate(candidate, role):
required = set(role["required_skills"])
have = set(candidate["skills"])
skill_match = len(required & have) / len(required)
exp_ok = candidate["years_experience"] >= role["min_years"]
return round(skill_match * 0.7 + (0.3 if exp_ok else 0), 2)
Real systems use semantic matching rather than keyword overlap, but the structure is the same: parse, normalize, score.
Where it gets hard
Parsing quality: multi-column resumes and unusual formatting break naive extractors.
Bias: training data and proxy features can encode unfair patterns, so explainability and auditing matter.
Edge cases: career changers and non-linear paths often score poorly on rigid rules.
Build or buy?
A prototype is a great learning project. Production use means ongoing parser maintenance, fairness checks, and integrations. For teams that just need hiring done, an AI-based applicant tracking system like hiremore AI bundles screening with sourcing and scheduling.
Have you built a resume parser? What broke first? Share it below.
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