Canonical: Engineering Manager - MLOps & Analytics — Home based - Worldwide

About Canonical

Canonical develops open-source software and operating systems for enterprise and technology users. Its Ubuntu platform is used in cloud computing, data science, artificial intelligence, engineering and connected-device environments. The employer describes a distributed working model with periodic in-person team meetings. Its vacancies therefore span both software delivery and the business functions that support it. Applicants should check the location, time-zone and travel conditions attached to the individual role rather than assume every position is available from any country.

The position and its setting

Canonical lists Engineering Manager - MLOps & Analytics in Home based - Worldwide, under reference 4810491. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Department: Charm Engineering
• Employer listing date: 17 March 2023

Responsibilities and work described

• 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
• Development: 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
• Also be expected to be a positive influence on culture, facilitate technical delivery, and regularly reflect with your team on strategy and execution
• Collaboration: 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
• Management: a distributed team of engineers and its MLOps/Analytics portfolio
• Organize and lead the team’s processes
• Research / execution: 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

Essential requirements and candidate background

• A track record of professional experience of software delivery
• Professional python development experience, preferably with a track record in open source
• A understanding of the machine learning space, its challenges and opportunities to improve
• Experience designing and implementing MLOps solutions
• An academic track record from both high school and preferably university
• Willingness to travel up to 4 times a year for internal events

Preferred experience and additional strengths

• The following skills may be helpful to you in the role, but we don't expect everyone to bring all of them
• Experience: machine learning libraries, or tools
• Track record of building highly automated machine learning solutions for the cloud
• Experience: building machine learning models
• Experience: container technologies (Docker, LXD, Kubernetes, etc.)
• Experience: public clouds (AWS, Azure, Google Cloud)
• Experience: the Linux and open-source software world
• Knowledge: 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

Location, schedule and contract conditions

For this overseas or regional listing, confirm work authorisation and any location restrictions with the employer. The location does not establish visa sponsorship or permission to work remotely from India.

• Work: from home with global travel for 2 to 4 weeks per year for internal and external events
• Advertised location: Home based - Worldwide

Benefits mentioned by the employer

• We consider geographical location, experience, and performance in shaping compensation worldwide
• We revisit compensation annually (and more often for graduates and associates) to ensure we recognise performance
• In addition to base pay, we offer a performance-driven annual bonus
• We provide all team members with additional benefits, which reflect our values and ideals
• We balance our programs to meet local needs and ensure fairness globally
• 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

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Engineering Manager - MLOps & Analytics vacancy is not an offer for reference 4810491; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Canonical

Applications follow the official employer page linked below.

Open the official Canonical posting below and match the title and reference 4810491.

Compare your qualifications and experience with the essential conditions for the Home based - Worldwide position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• Kubernetes
• Docker
• AWS
• GCP
• Azure
• AI/ML
• Google Cloud
• Linux
• machine learning