Engineering Manager - MLOps & Analytics

Canonical
Glasgow
1 month ago
Applications closed

Related Jobs

View all jobs

Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR...

Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR...

Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR...

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hyb...

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hyb...

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hyb...

Join to apply for the Engineering Manager - MLOps & Analytics role at Canonical

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 whole team is focused, productive and unblocked.

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 team on strategy and execution.

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.

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

What we are looking for in you

  • 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

Additional skills that you might also bring

  • 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

What we offer you

  • Distributed work environment with twice-yearly team sprints in person - we've been working remotely since 2004!
  • 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 from your team and others
  • Priority Pass for travel and travel upgrades for long haul company events

About Canonical

Canonical is a pioneering tech firm that is at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open source projects and the platform for AI, IoT and the cloud, 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, and will challenge you to think differently, work smarter, learn new skills, and raise your game. Canonical provides a unique window into the world of 21st-century digital business.

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.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.