Python and Kubernetes Software Engineer - Data, Workflows, AI/ML & Analytics
AI summary of the role
Canonical is hiring Python and Kubernetes engineers to build open source data analytics and MLOps solutions for public cloud and private infrastructure.
What you’ll do
- Design, build and maintain solutions deployed on public and private clouds and local workstations
- 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
What you’ll bring
- Professional or academic software delivery using Python
- Undergraduate degree in a technical subject or compelling alternative narrative
- Experience with Linux (Debian or Ubuntu preferred)
- Professional written and spoken English with excellent presentation skills
Technologies
Python · Kubernetes · Kubeflow · MLFlow · DVC · Feast · Apache Airflow · Apache Trino · ClickHouse · Docker
About Canonical
Canonical publishes Ubuntu, the leading enterprise Linux OS for cloud and edge, and sells subscriptions, managed services, and open-source infrastructure software.
Bootstrapped · 1000–2000 people
Source and classification
Production engineering · Evidence for this classification:
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, Workflows, AI/ML and Analytics Solutions to join our teams building open source solutions for public cloud and
More from the job description
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, Workflows, 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 end-to-end data analytics and MLOps solutions composed of popular, open-source, machine learning tools, such as Kubeflow, MLFlow, DVC, and Feast. You may also work on ETL, data governance and visualization tools like Apache SuperSet, dbt, workflow orchestration tools such as Airflow and Temporal, or data warehouse solutions such as Apache Trino, or ClickHouse. These solutions may be run on servers or on the cloud, o [... source excerpt omitted ...] hines 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 hours! Successful candidates will join a team
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