Google’s Cloud Jobs API, uses machine-learning techniques to understand and anticipate a job seeker’s preferences. It’s designed for enterprise career sites, applicant tracking sites and job boards.
Google has released a new Application Programming Interface (API) that it says can help organizations improve their employee recruitment process.
The company’s Google Cloud Jobs API is intended for enterprise career sites, applicant tracking systems and job boards. The API use machine-learning techniques to understand and anticipate a job seeker’s preferences and match them to available or even anticipated job openings.
The API is the result of ongoing efforts at Google to try and tap machine-learning methods to improve the manner in which people search for jobs and in which companies hire them.
It leverages a proprietary occupation ontology that includes 30 broad job categories like accounting and finance and another separate proprietary skill ontology comprised of over 50,000 hard and soft skills related to those job categories. The API then uses relational models to identify and group similar skills and occupational families.
According to Google, the API enables an intuitive job search experience that anticipates what a job seeker might be looking for and serves up recommendations on existing openings or suggests new job opportunities to explore.
“Cloud Jobs API uses machine learning to understand how job titles and skills relate to one another and what job content, location, and seniority are the closest match to a jobseeker’s preferences,” said Rob Craft, group lead for Google Cloud Machine Learning in an announcement on the company’s Cloud Platform blog.
The API addresses issues like those that arise when job postings are worded in company-specific or industry-specific jargon that a jobseeker may not have known to search for. The company points to an example where a jobseeker searching for a DevOps job on a career site might also get results for Site Reliability Engineer positions.
The API is also designed to interpret the different ways in which an organization might refer to the location of a job opening. For example, it can correctly interpret references to Research Triangle Park and the Bay Area and map them to the appropriate geographic location so users have more options for filtering jobs based on commute times and distance.
The Cloud Jobs API is one of several announcements that Google made this week involving its Cloud Machine Learning service. Other announcements include the launch of new hardware and pricing options and broader availability of its Cloud Translation API.
Starting 2017, customers of Google’s Cloud Machine Learning services will have the option of signing up for specialized Graphics Processing Units (GPUs) for running complex machine learning applications. The GPUs will be capable of delivering the tens of teraflops of performance required for running deep learning, physical simulation and similar processor intensive applications, according to Google.
On the pricing front, Google has reduced prices on its Cloud Vision API for image recognition apps by up to 80 percent as a result of what the company described as the substantial performance gains it has achieved from new hardware customized for machine learning.
In addition, Google this week announced broad availability of a premium edition of its Cloud Translation API featuring substantially lower error rates and support for eight languages.
