Google announced its Google Cloud Machine Learning Group to be led by two machine-learning experts: Fei-Fei Li and Jia Li. The group will focus on delivering cloud-based machine learning software to businesses.

The new group evolves from Google’s Cloud Machine Learning alpha application it launched in March.

In conjunction with announcing the new group, Google also introduced the new Google Cloud Jobs API to help people advance their careers.

“Over the past year, Google has developed a new machine-learning model that has the potential to greatly improve the recruitment efforts of any company,” writes Rob Craft, group lead for Google Cloud Machine Learning, in a corporate blog posting.

The new 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. The API is intended for job boards, career sites, and applicant tracking systems. Early adopters of Cloud Jobs API are the recruiting companies Jibe, Dice, and CareerBuilder.

To support its machine learning software and more complex workloads, Google Cloud will offer more hardware choices. Beginning in 2017, customers of Google Cloud Machine Learning and Google Compute Engine will be able to use graphics processing units (GPUs), which are specialized processors capable of handling the complexities of machine learning applications.

Enterprises can access Google’s GPU machines from anywhere, only paying for what they need. GPUs were originally created for computer graphics, but their abilities have proven useful in machine learning.

Most of the cloud titans already offer GPUs, including Amazon Web Services (AWS), Microsoft Azure, and IBM Cloud.

Google’s Cloud Machine Learning is based on a library called Tensorflow, which Google open-sourced in September. Tensorflow lets developers build machine learning models and then scale them to production.