Job Searcher

Job Searcher Back to Articles Job Searcher Community Article Published June 6, 2026 Upvote 6 Emre emrekuruu Follow build-small-hackathon Job hunting as a new grad is a full-time job by itself. You sift through hundreds of postings every week to find a handful worth applying to. You click "Easy Apply" until your eyes hurt. You write the same cover letter forty times. By month two of a search, you're applying to roles you wouldn't take, in industries you don't care about, because at that point the cost of thinking about each listing is higher than the cost of submitting to one. Watch the short tour: drop a resume, watch the queries stream, read the per-job reasoning. How it works A run has three steps. 1. Queries. The student reads the resume and the preferences you set (job type, work modality, location, free-form notes) and drafts a small set of LinkedIn-shaped search queries, reasoning out loud as it goes. 2. Search. Those queries hit LinkedIn through JobSpy, one at a time. 3. Scoring. For each posting, the model reads the`(resume, job)` pair and writes a five-dimension fit score: 4. - skills match - experience relevance - education and certifications - industry / domain fit - seniority alignment Figure 1. End-to-end steps of the framework. What you get back isn't a list of fifty roles. It's a small shortlist with defensible reasoning. You can read why the model thinks the second-ranked job beats the third. Technical Details Dataset Curation - The teacher and the student The teacher is DeepSeek V4 Pro. Strong at structured reasoning, willing to follow a strict output schema, cheap enough to run once over a large corpus offline. It is used as a label generator, not as an inference-time dependency. The student is Qwen3-8B. Small enough to fit on a single ZeroGPU slice once quantized to Q4_K_M, large enough to absorb the teacher's structured judgement. The corpus came from a closed loop, resume-aware end-to-end: - Resumes. 2,500, built