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Artificial Intelligence • Big Data • Cloud • Information Technology • Machine Learning • Software
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We in the Machine Learning product area in the Activation, Retention, Conversion studio are focused on building robust and scalable machine learning solutions that can personalize activation, retention and conversion funnels to improve important business metrics like SUBS and MAU. Through our messaging platform as well as other discovery & conversion surfaces, we communicate with users to connect them with valuable audio content and to help the business grow.
We are looking for a passionate Junior Machine Learning Engineer to help us accomplish our mission: enable impactful ML optimisation opportunities in new domains (Awareness & Acquisition, Commerce & Customer Support, Free & Paid Products) and bootstrap the GenAI Strategy of the Subscriptions Mission.
What You'll Do
- Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development
- Collaborate with a multi-functional agile team spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and relevant ways
- Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users
- Help drive optimisation, testing, and tooling to improve quality
- Be part of an active group of machine learning practitioners in your mission and across Spotify
- You'll work on projects like; optimising the ad load time and features usage friction for free users to balance retention and conversion, and developing GenAI prototypes with our partner squads
Who You Are
- You have at least 2 years of experience in applied Machine Learning Engineering
- You have a strong background in machine learning, theory, and practice
- You are comfortable explaining the intuition and assumptions behind ML concepts
- You have hands-on experience implementing and maintaining production ML systems in Python, Scala, or similar languages
- Experience with Pytorch and TensorFlow is also a plus
- You are experienced with building data pipelines, and you are self-sufficient in getting the data you need to build and evaluate your models
- You preferably have experience with cloud platforms like GCP or AWS
- You care about agile software processes, data development, reliability, and focused experimentation
- You have a desire to drive business impact
Where You'll Be
- This role is based in London, UK or Stockholm, Sweden
- We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home. We ask that you come in 3 times per week.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. 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 your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of 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 subscription service.
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