Senior Machine Learning Engineer, GenRecs, Personalization

The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Made For You, Discover Weekly and Daily Mix.Where You’ll Be (choose what is best based on the role / team!)We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.This team operates within the Eastern Standard time zone for collaboration. What You'll Do Be a technical leader within the team you work with and within Spotify in generalCoordinate technical projects across teams within SpotifyFacilitate collaboration with other engineers, product owners, and designers to solve interesting and challenging problems for delivering various media worldwideBe a valued member of an autonomous, cross-functional agile teamArchitect, design, develop, and deploy ML modelsBe a leader in Spotify’s ML community and work collaboratively and efficiently on existing platforms and systems. Who You Are You have experience being a technical leader or mentorExperience training, improving and operating internet-scale ML modelsYou have a deep understanding of ML systems and infrastructureYou have experience in Tensorflow or PyTorchAt least, some hands on experience with technologies such as Kubernetes or RayExperience troubleshooting model training and deployment across hardware ecosystems that mix CPU and GPU, including distributed multi-node model trainingExperience with model compression (quantization, weight clustering, pruning) and model computational efficiency optimization (layer fusing, hardware specific optimizations)Experience optimizing model inference for different GPU typesYou have experience with recommender systems and user behavior modeling, experience with generative models is a plusExperience with model fine-tuning techniques such as quantized low-rank adaptation (QLoRA) is a plus Where You'll Be We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work locationThis team operates within the Eastern Standard time zone for collaboration Additional Information The United States base range for this position is $176,166 $251,666 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.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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