Python & Kubernetes AI/ML Data Engineer (Distributed)

Canonical; Seattle (WA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Personal learning and development预算 of

Annual compensation review

Travel opportunities for company-wides

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 analytics platforms for cloud and on-prem infrastructure.

You'll collaborate on end-to-end analytics solutions using tools like Kubeflow, MLFlow, DVC, Feast, and more, across Kubernetes and cloud environments. Our team is distributed globally, with in-person sprints and a strong culture of collaboration.

Qualifications

• Professional or academic software delivery using Python
• Undergraduate degree in a technical subject
• Excellent English communication skills
• Ability to travel up to two weeks twice a year

Responsibilities

• 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

Skills

Python

Kubernetes

Linux

English communication

Education

Undergraduate degree in a technical subject

Tools

Kubeflow

MLFlow

DVC

Feast

Apache Superset

dbt

Temporal

ClickHouse

Trino

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 analytics platforms for cloud and on-prem infrastructure.

You'll collaborate on end-to-end analytics solutions using tools like Kubeflow, MLFlow, DVC, Feast, and more, across Kubernetes and cloud environments. Our team is distributed globally, with in-person sprints and a strong culture of collaboration.