Remote Platform Engineer II - Machine Learning Infrastructure

Description:

• The Hendrix ML Platform team is focused on developing a robust platform for training and serving machine learning models across Spotify.
• This platform aims to streamline the productionization of AI and ML models by reducing the complexities involved in creating backend services for serving predictions and training models.
• Responsibilities include managing and maintaining large scale production Kubernetes clusters for ML workloads, including ML platform infrastructure and necessary dev ops.
• The role involves contributing to the Spotify ML Platform SDK and building tools for various ML operations.
• Collaboration with Machine Learning Engineers (MLE), researchers, and product teams is essential to deliver scalable ML platform tooling solutions that meet timelines and specifications.
• The position requires working independently and collaboratively on squad projects, often necessitating the learning and application of new technologies.
• The engineer will design, document, and implement reliable, testable, and maintainable solutions for ML infrastructure capabilities.

Requirements:

• Candidates must have 3+ years of hands-on experience implementing production ML infrastructure at scale using Python, Go, or similar languages.
• A minimum of 3+ years of experience working with a public cloud provider such as GCP, AWS, or Azure is required, with a preference for GCP.
• Knowledge of deep learning fundamentals, algorithms, and open-source tools such as Huggingface, Ray, PyTorch, or TensorFlow is necessary.
• An understanding of distributed training leveraging GPUs and Kubernetes is considered a good to have.
• A general understanding of data processing for ML is required.
• Experience with agile software processes and modular code design following industry standards is essential.

Benefits:

• This role is based in Toronto, providing a location for in-person meetings while allowing flexibility to work from home.
• The company offers the flexibility to work where you are most productive, accommodating both remote and in-office work arrangements.