Siri - Software Engineer in Data (Part Time) at Apple ( Cambridge, Cambridgeshire, United Kingdom )

SUMMARYApple's Machine Learning and AI team transform every Apple product and because we fully integrate hardware and software, we can collaborate to deliver amazing experiences while protecting user data. Siri is the voice of Apple products and it's launch was a defining moment in the history of Artificial Intelligence. Hundreds of millions of people now use Siri to send a message, play their favourite song or even take a selfie. We're looking for a passionate individual able to improve the processing, analysis and preparation of huge data sets used to train Siri's machine learned models. You will love big-data technologies, including data exploration, visualisation, distributed processing, and applications at scale.DESCRIPTIONWorking as part of an extraordinary team, including world renowned academics, top software engineers and expert machine learning practitioners, we are passionate about applying groundbreaking techniques to conversational dialogue. Siri processes more than a billion requests every week and good data is the at heart of this engine. Each commit you make has the potential to impact Siri users worldwide through the creation of better data. Our work powers state of the art machine learning models and data visualisations. You will work closely with ML feature teams, engineer distributed data pipelines and use your understanding of data annotation and how it is used in building real world ML models to improve our systems. To bring new ideas and innovation any research experience involving the use or creation of datasets, corpora, and annotation is a plus. Apple is an inclusive employer and this position is open to applicants seeking a permanent position on reduced hours. We require a minimum commitment of 20 hours per working week. If this is you, we'd love to hear from you.QualificationsOO Programming & Design (Scala or Java) Data Engineering (Spark, Tableau, etc.) Ability to consult with Machine Learning Scientists