.Canonical is a leading provider of open source software and operating systems to the global enterprise and technology markets. Our platform, Ubuntu, is very widely used in breakthrough enterprise initiatives such as public cloud, data science, AI, engineering innovation, and IoT. Our customers include the world's leading public cloud and silicon providers, and industry leaders in many sectors. The company is a pioneer of global distributed collaboration, with 1000+ colleagues in 70+ countries and very few roles based in offices. Teams meet two to four times yearly in person, in interesting locations around the world, to align on strategy and execution.The company is founder-led, profitable, and growing. We are hiring Python and Kubernetes Specialist Engineers focused on Data, AI/ML, and Analytics Solutions to join our teams building open source solutions for public cloud and private infrastructure.As a software engineer on the team, you'll collaborate on an end-to-end data analytics and mlops solution composed of popular, open-source, machine learning tools, such as Kubeflow, MLFlow, DVC, and Feast. You may also work on workflow, ETL, data governance, and visualization tools like Apache SuperSet, dbt, and Temporal, or data warehouse solutions such as Apache Trino or ClickHouse. Your team will own a solution from the analytics and machine learning space, and integrate with the solutions from other teams to build the world's best end-to-end data platform. These solutions may be run on servers or on the cloud, on machines or on Kubernetes, on developer desktops, or as web services.We serve the needs of individuals and community members as much as the needs of our Global 2000 and Fortune 500 customers; we make our primary work available free of charge and our Pro subscriptions are also available to individuals for personal use at no cost. Our goal is to enable more people to enjoy the benefits of open source, regardless of their circumstances.Location: This initiative spans many teams that are home-based in EMEA, Americas, and APAC time zones, so we can accommodate candidates in almost any location. We believe in distributed collaboration but we also try to ensure that colleagues have company during their work hours! Successful candidates will join a team where most members and your manager are broadly in the same time zone so that you have the benefits of constant collaboration and discussion