Platform Engineer II - Machine Learning Infrastructure

What You'll Do

• Manage and maintain large scale production Kubernetes clusters for ML workloads, including ML platform infrastructure and necessary dev ops.
• 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.
• Designs, documents and implements reliable, testable and maintainable solutions ML infrastructure capabilities.

Who You Are

• You have 3+ years of hands-on experience implementing production ML infrastructure at scale in Python, Go or similar languages
• 3+ years of experience working with a public cloud provider such as GCP, AWS, or Azure. Preferably GCP.
• Knowledge of deep learning fundamentals, algorithms, and open-source tools such as Huggingface, Ray, PyTorch or TensorFlow
• Good 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

Where You'll Be

• This role is based in Toronto.
• 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.