Engineering
Machine Learning
Permanent
Toronto
The Hendrix ML Platform team is dedicated to developing a robust, Spotify-wide platform for training and serving machine learning models. This platform streamlines the productionization of AI and ML models by mitigating the incidental complexities involved in creating backend services for serving predictions and training models.

What You’ll Do

• Contribute to Spotify ML Platform SDK and build tools for various ML operations.
• Collaborate with Machine Learning Engineers (MLE), researchers, and various product teams to deliver scalable ML platform tooling solutions that meet the timelines and specifications of given requirements.
• Work independently and collaboratively on squad projects that often requires learning and applying new technologies that may go beyond existing skillsets.
• Manage and maintain large scale production Kubernetes clusters for ML workloads, including ML platform infrastructure and necessary dev ops.
• Designs, documents and implements reliable, testable and maintainable solutions ML infrastructure capabilities.

Who You Are

• You have 3+ years of hands-on experience productionize ML models either by collaborating with Knowledge of deep learning fundamentals, algorithms, and open-source tools such as Huggingface, Ray, PyTorch or TensorFlowGood to have an understanding of distributed training leveraging GPUs and Kubernetes
• You have a general understanding of data processing for ML
• You have experience with agile software processes and modular code design following industry standards
• You have hands-on experience implementing and maintaining production ML systems in Python, Scala, or similar languages

Where You’ll Be

• This role is based in Toronto, Canada
• 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.

Learn about life at Spotify
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.
Our global benefits
Extensive learning opportunities, through our dedicated team, GreenHouse.
Flexible share incentives letting you choose how you share in our success.
Global parental leave, six months off - for all new parents.
All The Feels, our employee assistance program and self-care hub.
Flexible public holidays, swap days off according to your values and beliefs.

• Contribute to Spotify ML Platform SDK and build tools for various ML operations.
• Collaborate with Machine Learning Engineers (MLE), researchers, and various product teams to deliver scalable ML platform tooling solutions that meet the timelines and specifications of given requirements.
• Work independently and collaboratively on squad projects that often requires learning and applying new technologies that may go beyond existing skillsets.
• Manage and maintain large scale production Kubernetes clusters for ML workloads, including ML platform infrastructure and necessary dev ops.
• Designs, documents and implements reliable, testable and maintainable solutions ML infrastructure capabilities