Machine Learning in Career Assistance and Job Application Automation: A Comprehensive Review

Abstract: Job hunting today is far more complex than it was a decade ago. Candidates must adapt resumes for each role, track multiple applications, and continuously upskill. This paper presents CareerSync, an intelligent system that automates resume parsing, job matching, skill gap analysis, career path recommendation, and job application submission. The system uses BERT-based NLP for accurate resume understanding, along with a hybrid ensemble of Random Forest, SVM, and LSTM models to improve job-candidate matching performance. Additionally, it integrates a recommendation engine to suggest personalized career paths and learning opportunities based on identified skill gaps. A Robotic Process Automation (RPA) module enables seamless interaction with job portals, reducing manual effort in the application process.

Keywords: Career assistance, job recommendation, NLP, BERT, LSTM, resume parsing, RPA