Engineering Manager - MLOps Analytics

Canonical

Remote

Full Time

Posted March 31, 2026

Not Specified

Remote

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Job Description

An Engineering Manager at Canonical leads and develops a remote, distributed team working on MLOps and open-source machine learning tools, ensuring technical excellence, team growth, and successful project delivery in a collaborative environment.

Key Responsibilities

Manage a distributed team of engineers and oversee the MLOps Analytics portfolio.
Organize and lead team processes to achieve objectives.
Conduct one-on-one meetings and measure team health indicators.
Review code and provide architectural leadership.
Mentor and develop team members to achieve professional growth.
Collaborate with other managers, product managers, and architects to produce an engineering roadmap.
Ensure high-quality engineering practices, documentation, and performance optimization.
Represent Canonical and its solutions at conferences and community events.

Requirements

A proven track record of professional experience of software delivery
Professional Python development experience, preferably with a track record in open source
A proven understanding of the machine learning space, its challenges and opportunities to improve
Experience designing and implementing MLOps solutions
An exceptional academic track record from both high school and preferably university
Willingness to travel up to 4 times a year for internal events
Manage a distributed team of engineers and its MLOps Analytics portfolio
Organize and lead the team’s processes in order to help it achieve its objectives
Conduct one-on-one meetings with team members
Identify and measure team health indicators
Interact with a vibrant community
Review code produced by other engineers
Attend conferences to represent Canonical and its MLOps solutions
Mentor and grow your direct reports, helping them achieve their professional goals
Work from home with global travel for 2 to 4 weeks per year for internal and external events
Fluency in the programming language, architecture, and components that their team uses, specifically popular open-source machine learning tools like Kubeflow, MLFlow, and Feast
Experience with container technologies such as Docker, LXD, Kubernetes, etc.
Experience with public clouds including AWS, Azure, and Google Cloud
Experience working on Linux and open-source software
Working knowledge of cloud computing
Passionate about software quality and testing
Experience working on a distributed team on an open source project, including community open source contributions

Benefits & Perks

Compensation is based on geographical location, experience, and performance, with annual reviews and more frequent reviews for graduates and associates

Performance-driven annual bonus

Distributed work environment with twice-yearly in-person team sprints

Personal learning and development budget of USD 2,000 per year

Annual compensation review

Recognition rewards

Annual holiday leave

Maternity and paternity leave

Employee Assistance Programme

Opportunity to travel to new locations to meet colleagues

Priority Pass for travel and travel upgrades for long haul company events