While our HR technology is sophisticated, our solution is simple. Jobiak enables employers and talent acquisition partners to easily publish and optimize job listings on Google for Jobs.
Our state-of-the-art Machine Learning (ML) eliminates the technology challenges that previously limited access to this incredible new recruiting channel, empowering clients to achieve extraordinary results that no other recruitment technology can deliver.
Job IDs are hard to recognize since a job page is usually littered with various types of IDs that resemble a job ID. Jobiak’s learning algorithms can accurately separate and extract the correct job ID from the rest.
Identify distinct components of Job Identifier (Ref Job, Job Id etc)
Hiring company name can appear anywhere on a job description page. Sometimes part of a large blob of text, sometimes as a image logo on the page or simply implied by the URL. The presence of other company names (like the hosting job board) or company name like entities make it even more difficult to accurately identify the hiring company. Jobiak’s sophisticated natural language processing and modeling techniques are capable of automatically distinguishing the correct company name from others. This is aided by Jobiak’s proprietary visual/structural parsing technology as well as millions of carefully curated and labelled data.
HTML Structure Analysis
NLP
X-Paths
Weighting Heuristics
Random Decision Forest
N-Gram Model
Job titles are unstructured and can appear anywhere on a job description page often along with other entities like job location, requisition number etc. making it extremely difficult to automatically extract. Jobiak’s sophisticated natural language processing and modeling techniques utilize 100s of visual, structural and semantic features to recognize and extract job titles with a high degree of accuracy from any unstructured web page. This is aided by Jobiak’s proprietary visual/structural parsing technology as well as millions of carefully curated and labelled data items.
Patterns/Regular Expressions
NLP
N-Gram Model
Lookup table
HTML Structure Analysis
Text Mining (Tf-Idf)
Random Decision Forest
Weighting Heuristics
Remove Non-relevant sections (similar jobs, more jobs etc)
Identify distinct components of Job titles(Ref Job, Job Id etc)
Locations are unstructured, can appear anywhere on the page, often incomplete and along with other entities like job title or in the middle of a large description making it difficult to extract. Jobiak’s sophisticated natural language processing and modeling techniques are capable of automatically identifying job locations anywhere on the page with a high degree of accuracy as well as canonicalizing it based on contextual information. This is aided by Jobiak’s proprietary visual/structural parsing technology as well as millions of carefully curated and labelled data.
Patterns/Regular Expressions
XPaths
N-Gram Model
Weighting Heuristics
Random Decision Forest
Identify Location Component (Cities, states, Regions, Countries)
Accurately identifying description is a hard task. Descriptions are made up of large portions of text, often with multiple sections. Accurately identifying text that is part of a job description and identifying the beginning and end of description sections becomes hard, even for human reviewers. Jobiak employs sophisticated machine learning techniques to identify various sections and topics that are part of the description and accurately classify sections that are part of the description. The technology also uses various algorithms to determine the boundaries of the description so as to accurately extract a description in it’s entirety, no more or no less than what is actually the description.
Unsupervised Topic Model (Latent Dirichlet Allocation)
Sentence classification Model (Random Decision Forest)
Job Description Detector (density based algorithm)
Decision Hints
Jobiak’s algorithms can accurately identify salaries in job descriptions usually written in various formats (ranges), currencies and units (hourly, annually).
N-Gram Model
Xpaths
Weighting Heuristics
Identify Salary Components (periodic, range, simple, descriptive etc)
Various job types associated with a job are identified whether it is explicitly present in the job page or inferred through context.
Deep Neural Network
NLP
Weighting Heuristics
Xpaths
N-Gram Model
Identify distinct components of Job type (Full Time, Contractor, Part time etc)
Jobiak’s algorithms can accurately detect and distinguish between various kinds of dates like posted dates, validity dates, age etc
Identify distinct components of posting date (Posted Date, Posted since etc)
Xpaths
N-Gram Model
Identify distinct components of valid through date (Closes on, Valid through etc)
N-Gram Model
Xpaths
Supervised Model For Certain Tags
N-Gram Model
Xpaths
Jobiak’s optimization technology is built using sophisticated machine learning algorithms trained using millions of job postings and their online performance over a long period of time. Jobiak has built knowledge structures such as association graphs of titles, skills, descriptions using sophisticated text processing techniques. Convolutional models trained on this data accurately recommend proven job optimizations required to improve online visibility for job listings.
Identify Location, Job ID & Skill Components
Supervised model for certain tags
Power mean & graph embeddings
10 convolutional neural networks
Jobiak automatically optimizes your job posts for high ranking using machine-generated keywords, titles and descriptions, based on analysis of both real-time information and learnings from millions of monitored postings.
Our AI-platform executes over 25 specialized SEO techniques both on the front-end of the post that job-seekers see on Google for Jobs, and in the background, to optimize the underlying code.
Over 80,000 man-hours went into developing our patent-pending recruitment technology, which was built by exceptional engineers with deep expertise in the recruiting and Machine Learning industries. Our Artificial Intelligence (AI) model is constantly trained to generate high-performing keywords and make real-time SEO adjustments based on local market demand.
