Staff Machine Leaning Engineer - remote

The Freemium R&D team oversees the entire user journey on Spotify and ensures we engage with people in innovative ways, every step of the way. Our team grows Spotify’s audience by finding future listeners around the world and delivering the right value to them, at the right time. With research, product development, product design, engineering, and marketing all collaborating in one organization, we’re able to quickly create meaningful features and services for millions of people around the world, resulting in joyful, long-lasting relationships with Spotify. We are looking for a Staff Machine Learning Engineer within the Freemium mission who will work on providing the right value to the right user at the right time. We aim to customize the Spotify experience to fit each of our users expectations, creating an engaging experience and growing the Spotify audience.

What you'll do

• Support the engineering team in formulating the technical vision and strategy for our ML-based optimization across the freemium funnel.
• Seek sophisticated data-related problems involving some of the most diverse datasets available and resolve feasibility of projects through quick prototyping with respect to performance, quality, time and cost using Agile methodologies
• Architect outstanding infrastructure (platforms, tools, and approaches) to accelerate our research and prototypes to the product phase and set up efficient training, deployment, optimization, and testing of models.
• Apply machine learning to build product features that drive tangible business impact
• Be a leading voice in an active community of ML practitioners across Spotify and improve existing state-of-the-art tooling in the Spotify ecosystem (TensorFlow, DataFlow, python-beam, Google Cloud Platform)
• Contribute to our team-wide product conceptualization in collaboration with engineers, researchers, product managers and tech leads on the team.
• Help drive optimization, testing and tooling to improve data quality

Who you are:

• PhD or M.Sc. in Machine Learning, or related field
• You have 4+ years of machine learning product development experience demonstrating large scale data processing technologies (e.g. TensorFlow, SciKit learn, Dataflow, Hadoop, Scalding, Spark, Storm)
• You have experience in using ML techniques to optimize customer facing product features
• You have a strong mathematical background in statistics and machine learning
• You care about agile software processes, data-driven development, reliability, and responsible experimentation
• You preferably have machine learning publications or work on open source to share with us

Where you'll be:

• We are a distributed workforce enabling our band members to find a work mode that is best for them!Where in the world? For this role, it can be within the Americas region in which we have a work location.
• Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more informabout about our Work From Anywhere options here.
• Working hours? We operate within the EST/EDT time zone for collaboration

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. 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 with a community of more than 345 million users.