A proper résumé builder can help you stand out from the crowd. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Landing a new job starts with a slam-dunk CV, but Word or Google Docs will only take you so far. In an era of AI-assisted Application Tracking Systems (ATS) where applicants are rejected before a human ever lays eyes on their work experience, the best résumé builders could help you stand out from the crowd. Yes, they'll give your résumé a pretty face with unique templates and attention-grabbing fonts, but résumé builders shouldn't just give you a PDF and send you on your way. A good online résumé builder will give tips and insights into how you can bolster your CV, as well as tools for managing your documents when applying to multiple jobs.

The extraction of information from semi-structured text, such as resumes, has long been a challenge due to the diverse formatting styles and subjective content organization. Conventional solutions rely on specialized logic tailored for specific use cases. However, we propose a revolutionary approach leveraging structured Graphs, Natural Language Processing (NLP), and Deep Learning. By abstracting intricate logic into Graph structures, we transform raw data into a comprehensive Knowledge Graph. This innovative framework enables precise information extraction and sophisticated querying. We systematically construct dictionaries assigning skill weights, paving the way for nuanced talent analysis. Our system not only benefits job recruiters and curriculum designers but also empowers job seekers with targeted query-based filtering and ranking capabilities.

Queens' tech industry is growing, but the diversity of our tech workforce still lags significantly behind the overall diversity of our borough. To help break barriers to entry for members of underrepresented & marginalized communities, join Borough President Donovan Richards Jr. for his inaugural Queens Tech Fair. The event includes a job skills assessment from leading artificial intelligence software firm SkyHive, mentoring & networking opportunities, on-site support services, interactive activities, a group discussion on breaking barriers & more. Prior to the event, all participants must pre-register with the name, email, & home zipcode. To maximize your career potential & to get the most out of the tech fair, participants are encouraged to get a FREE SkyHive job skills assessment.

BERT, RoBERTa, DistilBERT, XLNet: Which one to use? #KDnuggets The Absolute Basics of MLOps This article is for people who don't know a thing about MLOps or want to refresh their memory.

There is a high demand for machine learning engineer jobs, but the hiring process is tough to crack. Companies want to hire professionals with experience in dealing with various machine learning problems. For a newbie or fresh graduate, there are only a few ways to showcase skills and experience. They can either get an internship, work on open source projects, volunteer in NGO projects, or work on portfolio projects. In this post, we will be focusing on machine learning portfolio projects that will boost your resume and help you during the recruitment process.

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Our approach is to (a) develop a methodology for an external audit of stability of predictions made by algorithmic personality tests, and (b) instantiate this methodology in an audit of two systems, Humantic AI and Crystal. Crucially, rather than challenging or affirming the assumptions made in psychometric testing -- that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job -- we frame our methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. In our audit of Humantic AI and Crystal, we find that both systems show substantial instability with respect to key facets of measurement, and so cannot be considered valid testing instruments. For example, Crystal frequently computes different personality scores if the same resume is given in PDF vs. in raw text format, violating the assumption that the output of an algorithmic personality test is stable across job-irrelevant variations in the input. Among other notable findings is evidence of persistent -- and often incorrect -- data linkage by Humantic AI.