Senior Machine Learning Engineer - Voice Personalization

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Daily Mix to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.
And now, users can interact with Spotify via the most natural communication paradigm: their voice! The Voice team is building platforms and features to unlock Voice driven experiences across the Spotify ecosystem. As the adoption of voice interactions grows, and as the C19 lockdowns ends, we see an opportunity to meet users' needs and create new habits. Join us as we build a world class platform for all things Voice (wakeword, ASR, NLU, NLP, NLG, and TTS) that get to the core of personalized experiences. We’re looking for t-shaped engineers who will play an important part in launching new features, improving performance, and building magical user experiences.

What You'll Do

• Be part of an active group of machine learning practitioners (across Spotify) collaborating with one another
• 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
• Contribute to designing, building, evaluating, shipping, and refining Spotify’s Voice products by hands-on ML development
• Drive optimization, testing, and tooling to improve quality
• Identify opportunities for platformization and communicate requirements to platform teams
• Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization

Who You Are

• You have a strong background in machine learning, with experience and expertise in NLP and NLU algorithms and systems such as NLU parsing models and language translation
• You understand a variety of machine learning algorithms including classification and sequence labeling models and content recommendation systems, and are familiar with reinforcement learning approaches such as online bandit models
• You have contributed code and models to large-scale, production NLU systems
• You have hands-on experience implementing scalable production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with TensorFlow, Kubeflow, PyTorch, Scikit-learn, XGBoost, etc. is a strong plus
• You are comfortable exploring data to deeply understand the problems and products you are working on and to develop good hypotheses for product improvements
• You care about shipping product, agile software processes, reliability, data-driven development, and disciplined but fast experimentation
• You keep up with state of the art research by reading papers, attending conferences, and participating in research communities
• You preferably have experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it, Scio, and cloud platforms like GCP or AWS

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.