Role
Engineering Manager - MLOps & Analytics at Canonical – Remote
Location: This is a Globally remote role.
What your day will look like
* 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
Responsibilities
* Be technically strong while focusing on running an effective team and developing colleagues
* Develop and review code as a leader, while ensuring the whole team is focused, productive and unblocked
* Help engineers grow, do meaningful work, and find professional and personal satisfaction
* Foster a positive culture, facilitate technical delivery, and reflect on strategy and execution with the team
* Collaborate with other Engineering Managers, product managers, and architects to produce an engineering roadmap with ambitious and achievable goals
* Be fluent in the programming language, architecture, and components used by the team (e.G., Kubeflow, MLFlow, Feast)
* Provide code reviews and architectural leadership;
uphold healthy engineering practices, documentation, quality and performance optimization;
ensure fair and clear management of a high-performing team
Qualifications
* A proven track record of professional software delivery
* Professional Python development experience, preferably with a track record in open source
* Strong understanding of the machine learning space, its challenges and opportunities for improvement
* Experience designing and implementing MLOps solutions
* Exceptional academic track record from high school and preferably university
* Willingness to travel up to 4 times a year for internal events
Additional skills
* 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 Linux and open-source software
* Working knowledge of cloud computing
* Passionate about software quality and testing
* Experience working on a distributed open source project or community contributions
* Demonstrated track record of Open Source contributions
What we offer you
* Distributed work environment with twice-yearly team sprints in person – remote since 2004
* Personal learning and development budget of USD 2,000 per year
* Annual compensation review
* Performance-driven bonus opportunities
* Annual holiday leave
* Maternity and paternity leave
* Employee Assistance Programme
* Opportunity to travel to meet colleagues at various locations
* Priority travel upgrades for long-haul company events
About Canonical
Canonical is a pioneering tech firm at the forefront of the global move to open source. As the company that publishes Ubuntu, we are changing the world on a daily basis. We recruit on a global basis and set a very high standard for people joining the company. We expect excellence – in order to succeed, we need to be the best at what we do.
Canonical has been a remote-first company since its inception in 2004. Work at Canonical is a step into the future, challenging you to think differently, work smarter, and learn new skills. Canonical provides a unique window into the world of 21st-century digital business.
Equal opportunity
Canonical is an equal opportunity employer. We are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products. Whatever your identity, we will give your application fair consideration.
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