Job Description

Position Overview

• Location Type: Hybrid (Toronto, with flexibility to work remotely)
• Employment Type: Permanent
• Salary: Not specified

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.

Requirements

• Experience: 3+ years of hands-on experience implementing production ML infrastructure at scale.
• Languages: Proficiency in Python, Go, or similar languages.
• Cloud Provider: 3+ years of experience working with a public cloud provider, preferably GCP.
• ML Fundamentals: Knowledge of deep learning fundamentals, algorithms, and open-source tools such as Huggingface, Ray, PyTorch or TensorFlow.
• Distributed Training: Understanding of distributed training leveraging GPUs and Kubernetes.
• Data Processing: General understanding of data processing for ML.
• Agile Development: Experience with agile software processes and modular code design following industry standards.

Responsibilities

• Manage and maintain large-scale production Kubernetes clusters for ML workloads, including ML platform infrastructure and necessary DevOps.
• 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 require learning and applying new technologies that may go beyond existing skillsets.
• Designs, documents, and implements reliable, testable, and maintainable solutions for ML infrastructure capabilities.

Application Instructions

• Not specified in the provided text.

Company Information

• Not specified in the provided text. The role is based in Toronto and offers flexibility to work where best suited.