AI Job Application Triage Assistant
A research-stage triage assistant for ranking role opportunities against structured skill evidence while keeping every real application decision manual.
What it is: A research direction for deciding which roles deserve attention through evidence-backed ranking before any application work begins.
What I built: Explored the ranking logic, approval boundaries, and evidence-model design for a safer application-triage workflow.
Current state: Research-stage work: the concept and architecture are ahead of the implementation maturity.
Why it matters: Explores a ranking layer that surfaces higher-signal roles before manual application work begins.
Category: Research / Experiment
Status: Research
Visibility: Public
This is a research-stage portfolio entry, not a claim of fully shipped production implementation.
What this project is
A research concept for deciding which opportunities deserve time before any manual application work starts.
Why I explored it
Application triage has a lot of repetitive judgment work: reading role pages, comparing them against real evidence, and deciding which opportunities are strong enough to justify attention.
Constraints
- Human oversight has to stay in place for high-risk or ambiguous outputs.
- The ranking logic should stay explainable instead of hiding behind vague scores.
- Any generated recommendation needs clear auditability.
Architecture direction
- Input normalization for job descriptions and evidence blocks.
- Ranking logic that compares role requirements against explicit profile signals.
- Review checkpoint before anything becomes a real application task.
Current state
This remains research work. The value so far is in clarifying the evidence model and triage boundaries, not claiming a finished application workflow.
Why it matters
The interesting part is the evidence discipline. If a triage system cannot explain why an opportunity surfaced, it is not useful enough to trust.
Key decisions
- Anchor ranking to evidence blocks so generated output does not drift into unsupported claims.
- Keep manual approval in the loop for any step that could affect real applications.
- Treat the workflow as research until evaluation quality is strong enough to trust the ranking logic.
What I'd improve next
The next improvement would be a small ranking eval set so triage quality can be tuned against real examples instead of intuition.