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Job Search Harness

Parallel LLM review of open job postings

I should not apply to most job postings! Even most remote product manager roles aren't right! Even if I manually configure all the filters at some job aggregator site, still - low hit rate!

So 90+% of the JDs I read are a waste of my time. With the right context and structure, we can get LLMs to churn through a lot of that grunt work.

So the goal is context and structure.

We need a process to evaluate companies against the verticals I am interested in. For now, that is an exclusionary filter (e.g. remote friendly. no ecommerce, crypto...)(much of this can be done deterministically). Then for each non-disqualified company we have the LLM evaluate each role for mutual fit, and produce a strucutred priority score (this is the part that wasn't feasible in 2022).

Arichitecture below, but I'm not posting the source on this one publicly because I'm using an undocumented API to source my job listings 🙂.

Harness architecture

Ingestion
Prefs docs (external)
Database (external)
Python harness (deterministic)
Review agent (Haiku / open weights)
Dashboard UI (user)
Step 0 — Ingestion
Ingest jobs, group by company
Populates companies table + jobs table. Runs outside the harness (e.g. a scraper/harvester).
Prefs Docs (external)
company prefs
role prefs
accomplishments
Database (external)
Python Harness — deterministic control flow
Review Agents
companies table
pull
unevaluated
1Send unevaluated companies
Review against company prefs, via Batching Agent
dispatch
return
Company Review Agent(s)
Haiku · APPROVE / DISQUALIFY
push
evaluations
2Update DB with company evaluations
Which companies are interesting → which jobs should be evaluated
jobs table
pull
unevaluated
3Send unevaluated jobs
Review against role prefs + accomplishments, via Batching Agent
dispatch
return
Job Review Agent(s)
Haiku · fit scoring
push
evaluations
4Update DB with job evaluations
high pri / low pri / skip
both tables
feed the dashboard
Dashboard UI
5Job seeker reviews
Prioritized list of job opportunities; applies manually