Job Searcher

Job hunting for new graduates can be an overwhelming task, but a newly developed system aims to streamline the process by using AI to generate a shortlist of job opportunities with detailed reasoning for each match. This framework involves a three-step process where a model reads a candidate's resume and preferences to create LinkedIn-shaped search queries, which are then executed to return job postings. These postings are scored on five dimensions: skills match, experience relevance, education and certifications, industry/domain fit, and seniority alignment. The system employs DeepSeek V4 Pro as a "teacher" to generate labels and Qwen3-8B as a "student" to absorb these judgments, resulting in a curated dataset of resumes and job postings. The training process uses two separate LoRA runs to improve performance, and the model operates on a HuggingFace ZeroGPU Space, allowing for efficient job fit evaluations. This innovative approach not only reduces the time and effort involved in job searching but also provides transparency and defensible reasoning behind each job match.

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