Remote Data & AI Platform Engineer — Python + Kubernetes

Remote; San Francisco (CA); Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Distributed work environment

Learning budget USD 2,000/year

Annual compensation review

Maternity and paternity leave

Employee Assistance Programme

Job summary

Canonical is hiring Python and Kubernetes Specialist Engineers focused on Data, AI/ML and Analytics Solutions to join our teams building open source data platforms for public cloud and private infrastructure. You will work on end-to-end analytics and MLops solutions using Kubeflow, MLFlow, DVC, Feast, and related tools, across cloud, Kubernetes, or local environments.

The role supports a distributed, remote-first team across time zones, with periodic in-person gatherings.

Qualifications

• Professional or academic software delivery using Python.
• Exceptional academic track record from high school and university.
• Undergraduate degree in a technical subject or a compelling alternative path.

Responsibilities

• Develop understanding of the Linux stack from kernel to application layer.
• Design, build and maintain solutions for public and private clouds and local workstations.
• Master distributed systems concepts such as observability and tracing.
• Work with Kubernetes and open source ML tools.
• Collaborate with a distributed team of engineers, designers and product managers.
• Debug issues and interact with upstream and Ubuntu communities.
• Generate and discuss ideas and collaborate on solutions.

Skills

Python programming

Distributed collaboration

Kubernetes

Machine learning

Data analytics

Education

Undergraduate degree in a technical subject

Strong academic track record

Tools

Docker

LXD

Kubernetes

Job description

Canonical is hiring Python and Kubernetes Specialist Engineers focused on Data, AI/ML and Analytics Solutions to join our teams building open source data platforms for public cloud and private infrastructure. You will work on end-to-end analytics and MLops solutions using Kubeflow, MLFlow, DVC, Feast, and related tools, across cloud, Kubernetes, or local environments.

The role supports a distributed, remote-first team across time zones, with periodic in-person gatherings.