Machine Learning Engineer

Rave is a media-focused technology company located in Waterloo, Ontario. Rave's two main products are an artificial intelligence DJ capable of autonomously mixing music together and a synchronous media viewing platform on mobile and in VR.

Rave utilizes machine learning to process, analyzing, and artfully combine user-selected music into mixes, or "mashups". Users have created over a million mashups at, such as Thunder vs. Turn Down For What, Wild Thoughts vs. Sua Casa, and Napal Baji vs. Uptown Funk.

Regarding the Rave app, it's available on iOS, Android, Samsung Galaxy Gear VR, and Google Daydream VR. Rave enables users to enjoy content from YouTube, Vimeo, Viki, Google Drive, Dropbox, and RaveDJ. Rave syncs playback down to the millisecond, allowing users to create local speaker systems from their phones. When users are distant, they can chat over text and speak on VoIP while they watch videos and listen to music. Rave is a combination of the best elements of a media player and a messenger.

Job Summary

As a member of Rave’s industrial ventures team, you will develop and integrate cutting-edge, machine-learning technologies for use in a wide-range of Canadian industrial applications. Using primarily Tensorflow, you will develop software and contribute through the team’s Git version control system.

Job Description

  • High and low-level analysis of datasets collected through industrial partners

  • Application of machine learning technology to improve the performance of an existing industrial product

  • Experiment design with a focus on research and development to support the product

  • Maintaining clean and well-documented production-ready code

  • Occasional travel to industrial partner location

Required Skills

  • Python

  • Object-Oriented Programming

  • Experience using a Linux OS

  • Git

Preferred Skills

  • Background in software engineering or computer science or mathematics

  • Experience with machine learning technology and API’s


  • Familiar with Docker container Virtual Machines

  • Experience with industrial controls hardware and software (PLC’s, CAN bus, etc)

  • Examples of machine learning projects on your github