Python and Kubernetes Software Engineer for Data and AI at Canonical

Role overview

This role sits in Canonical's distributed engineering teams building open-source solutions for public clouds, private infrastructure and developer machines. The work spans data analytics and MLOps, from orchestration and data pipelines to services that run on Kubernetes or conventional servers.

The employer describes the position as home-based worldwide. Team assignment considers time-zone overlap with a manager and colleagues. The posting also expects travel to company events twice a year.

Responsibilities

• Design, build and maintain software that can run across public and private clouds, local workstations, servers and Kubernetes environments.
• Work on end-to-end analytics and MLOps solutions using open-source tools; the posting names Kubeflow, MLflow, DVC and Feast as examples.
• Contribute where needed to ETL, governance, visualisation and workflow systems, with examples including Superset, dbt, Airflow, Temporal, Trino and ClickHouse.
• Develop a working understanding of Linux from the kernel, networking and storage through the application layer, alongside distributed-system topics such as observability, identity and tracing.
• Debug technical problems and work openly with upstream projects and Ubuntu communities.
• Exchange ideas and coordinate implementation with engineers, designers and product managers across a distributed team.

Qualifications

• Professional or academic experience delivering software in Python, plus experience using Linux; Debian or Ubuntu familiarity is preferred.
• A technical undergraduate degree or a persuasive account of an alternative route into engineering, together with a strong academic record as requested in the posting.
• Strong written and spoken English, presentation and interpersonal skills for collaboration across a multinational team.
• Evidence of personal technology projects, self-directed learning, accountability and a willingness to exchange feedback.
• Ability to travel twice yearly to company events that may last up to two weeks.
• Helpful but not required: automated machine-learning systems or data pipelines, workflow orchestration, ML libraries, Docker, LXD, Kubernetes, AWS, Azure, Google Cloud, software testing or open-source contributions.

Technology in this role

• Python: Python AWS Azure Kubernetes Docker .
• AWS: Python AWS Azure Kubernetes Docker .
• Azure: Python AWS Azure Kubernetes Docker .
• Kubernetes: Python AWS Azure Kubernetes Docker .
• Docker: Python AWS Azure Kubernetes Docker .
• Linux: Python AWS Azure Kubernetes Docker .
• Airflow: Contribute where needed to ETL, governance, visualisation and workflow systems, with examples including Superset, dbt, Airflow, Temporal, Trino and .
• dbt: Contribute where needed to ETL, governance, visualisation and workflow systems, with examples including Superset, dbt, Airflow, Temporal, Trino and .
• Google Cloud: Helpful but not required: automated machine-learning systems or data pipelines, workflow orchestration, ML libraries, Docker, LXD, Kubernetes, AWS, Azure, Google Cloud, software testing or open-source .
• CAN: Design, build and maintain software that can run across public and private clouds, local workstations, servers and Kubernetes .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This Python and Kubernetes Software Engineer - Data, Workflows, AI/ML & Analytics position at Canonical is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “This role sits in Canonical's distributed engineering teams building open-source solutions for public clouds, private infrastructure and developer machines. The work spans data analytics and MLOps, from orchestration…” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the AI and data area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Design, build and maintain software that can run across public and private clouds, local workstations, servers and Kubernetes environments.”; “Work on end-to-end analytics and MLOps solutions using open-source tools; the posting names Kubeflow, MLflow, DVC and Feast as examples.”; “Contribute where needed to ETL, governance, visualisation and workflow systems, with examples including Superset, dbt, Airflow, Temporal, Trino and ClickHouse.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by.

Benefits

• Home-based distributed work, with in-person team sprints twice a year.
• A stated learning and development budget of USD 2,000 per year.
• Annual compensation review, performance-linked bonus or commission, and recognition rewards; compensation depends on location, experience and performance.
• Annual leave, parental leave and an employee assistance programme, subject to local benefit arrangements.
• Travel opportunities for company events, including the travel benefits described in the employer posting.

About Canonical

Canonical publishes Ubuntu and builds commercial and open-source technology for cloud, data, AI and connected-device environments. The company describes itself as globally distributed and says most colleagues work from home.

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