Engineering Manager - MLOps & Analytics

The role of an Engineering Manager at Canonical

As an Engineering Manager at Canonical, you must be technically strong, but your main responsibility is to run an effective team and develop the colleagues you manage. You will develop and review code as a leader, while at the same time staying aware of that the best way to improve the product is to ensure that the ...

You are expected to help them grow as engineers, do meaningful work, do it outstandingly well, find professional and personal satisfaction, and work well with colleagues and the community. You will also be expected to be a positive influence on culture, facilitate technical delivery, and regularly reflect with your ...

You will collaborate closely with other Engineering Managers, product managers, and architects, producing an engineering roadmap with ambitious and achievable goals.
We expect Engineering Managers to be fluent in the programming language, architecture, and components that their team uses, in this case popular open-source machine learning tools like Kubeflow, MLFlow, and Feast.
Code reviews and architectural leadership are part of the job. The commitment to healthy engineering practices, documentation, quality and performance optimisation is as important, as is the requirement for fair and clear management, and the obligation to ensure a high-performing team.

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

The following skills may be helpful to you in the role, but we don't expect everyone to bring all of them.
Hands-on experience with machine learning libraries, or tools.
Proven track record of building highly automated machine learning solutions for the cloud.
Experience with building machine learning models
Experience with container technologies (Docker, LXD, Kubernetes, etc.)
Experience with public clouds (AWS, Azure, Google Cloud)
Experience in the Linux and open-source software world
Working knowledge of cloud computing
Passionate about software quality and testing
Experience working on a distributed team on an open source project -- even if that is community open source contributions.
Demonstrated track record of Open Source contributions