Engineering Manager - MLOps & Analytics

Canonical
Glasgow
3 weeks ago
Applications closed

Related Jobs

View all jobs

Engineering Manager - MLOps & Analytics

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, H[...]

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, H[...]

Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote O[...]

Engineering Manager, Data Science – Growth & pLTV Lead

Engineering Manager - MLOps & Analytics

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.

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

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.