The Personalization team makes deciding what to playnext easier and more enjoyable for every listener. From Blend toDiscover Weekly, we’re behind some of Spotify’s most-lovedfeatures. We built them by understanding the world of music andpodcasts better than anyone else. Join us and you’ll keep millionsof users listening by making great recommendations to each andevery one of them. We ask that our team members be physicallylocated in the Central European time zone for the purposes of ourcollaboration hours. We are looking for a Machine Learning Engineer(MLE II) to join our product area of hardworking engineers that arepassionate about connecting new and emerging creators with usersvia recommendation algorithms. As an integral part of the squad,you will collaborate with engineers, research scientists, and dataengineers in prototyping and productizing state-of-the-art MLmodels that allow us to find the right audience for content that isstrategically important, such as fresh or timely content. WhatYou'll Do - Develop and implement production systems that enrichand improve our listeners’ experience on the platform - Contributeto designing, building, evaluating, shipping, and refiningSpotify’s product by hands-on ML development - Help driveoptimization, testing, and tooling to improve quality of ourrecommendations - Perform data analysis to establish baselines andinform product decisions - Collaborate with a cross functionalagile team spanning tech research, data science, productmanagement, and engineering to build new technologies and features- Stay up-to-date on the latest machine learning algorithms andtechniques Who You Are - You have professional experience inapplied machine learning - Extensive experience working in aproduct and data-driven environment (Python, Scala, Java, SQL, orC++, with Python experience required) and cloud platforms (GCP orAWS) - You have some hands-on experience implementing orprototyping machine learning systems at scale - You have experiencearchitecting data pipelines and are self-sufficient in getting thedata you need to build and evaluate models, using tools likeDataflow, Apache Beam, or Spark - You care about agile softwareprocesses, data-driven development, reliability, and disciplinedexperimentation - You have experience and passion for fosteringcollaborative teams - Experience with TensorFlow, pyTorch, and/orother scalable Machine learning frameworks - Experience withbuilding data pipelines and getting the data you need to build andevaluate your models, using tools like Apache Beam / Spark WhereYou'll Be - We offer you the flexibility to work where you workbest! For this role, you can be within the EMEA region as long aswe have a work location. *excluding France for now due to on-callrestrictions. - This team operates within the Central European timezone for collaboration. Spotify is an equal opportunity employer.You are welcome at Spotify for who you are, no matter where youcome from, what you look like, or what’s playing in yourheadphones. Our platform is for everyone, and so is our workplace.The more voices we have represented and amplified in our business,the more we will all thrive, contribute, and be forward-thinking!So bring us your personal experience, your perspectives, and yourbackground. It’s in our differences that we will find the power tokeep revolutionizing the way the world listens. At Spotify, we arepassionate about inclusivity and making sure our entire recruitmentprocess is accessible to everyone. We have ways to requestreasonable accommodations during the interview process and helpassist in what you need. If you need accommodations at any stage ofthe application or interview process, please let us know - we’rehere to support you in any way we can. Spotify transformed musiclistening forever when we launched in 2008. Our mission is tounlock the potential of human creativity by giving a millioncreative artists the opportunity to live off their art and billionsof fans the chance to enjoy and be passionate about these creators.Everything we do is driven by our love for music and podcasting.Today, we are the world’s most popular audio streaming subscriptionservice. #J-18808-Ljbffr
