Senior Machine Learning Engineer, Home Podcast

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Senior Machine Learning Engineer, Home Podcast

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Spotify Senior Machine Learning Engineer, Home Podcast

Join to apply for the

Senior Machine Learning Engineer, Home Podcast

role at

Spotify The Home Podcasts team within Spotify’s Personalization Mission focuses on what podcasts to recommend on Spotify’s Homepage and where, by building the model to rank and find the perfect podcast content, fully tailored to each user. We are looking for a Machine Learning Engineer who is passionate about personalization ML models, recommender systems and disciplines included but not limited to contextual bandits, causal inference, deep learning, and generative recommenders, which are actively used and expanded by our teams. Join us and you’ll keep millions of users listening by making great recommendations to the Spotify Homepage. For the purposes of collaboration, we ask that our team members operate in the Eastern time zone.

What You'll Do

Be a technical leader within the team you work with and within Spotify in general Coordinate technical projects across teams within SpotifyFacilitate collaboration with other engineers, product owners, and designers to solve interesting and challenging problems for delivering various media worldwide Be a valued member of an autonomous, cross-functional agile team Architect, design, develop, and deploy ML models that will serve podcast recommendations across the Home, Podcast Subfeed, and NPV surfaces Be a leader in Home’s ML community and work collaboratively and efficiently within Home’s existing platforms and systems.

Who You Are

You have experience being a technical leader or mentorYou have a strong background in machine learning, especially experience with recommender systems You have experience in designing and building ML systems at Spotify (including experience in spotify-kubeflow and salem) You are experienced with feature engineering and building scalable data pipelines in Scio You have a deep understanding of ML systems and infrastructure You have experience in Tensorflow or PyTorch. Experience with Kubeflow, Ray 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 location. This team operates within the Eastern Standard time zone for collaboration.

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. Seniority level

Seniority level Mid-Senior level Employment type

Employment type Full-time Job function

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