Python and Kubernetes Software Engineer - Data, AI / ML & Analytics

About Canonical

Canonical is a pioneering tech firm at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open source projects and the platform for AI, IoT and the cloud, we are changing the world of software. We recruit on a global basis and set a very high standard for people joining the company. We expect excellence - in order to succeed, we need to be the best at what we do. Most colleagues at Canonical have worked from home since its inception in 2004. Working here is a step into the future, and will challenge you to think differently, work smarter, learn new skills, and raise your game.

Canonical is an equal opportunity employer

We are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products. Whatever your identity, we will give your application fair consideration.

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 and in multiple time zones. We believe in distributed collaboration but we also try to ensure that colleagues have company during their work hourse! 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.

What your day will look like

• Develop your understanding of the entire Linux stack, from kernel, networking, and storage, to the application layer
• Design, build and maintain solutions that will be deployed on public and private clouds and local workstations
• Master distributed systems concepts such as observability, identity, tracing
• Work with both Kubernetes and machine-oriented open source applications
• Collaborate proactively with a distributed team of engineers, designers and product managers
• Debug issues and interact in public with upstream and Ubuntu communities
• Generate and discuss ideas, and collaborate on finding good solutions

What we are looking for in you

Additional Skills That Would Be Nice To Have

What we offer colleagues

#J-18808-Ljbffr