Edge AI Engineer at Canonical - Remote EMEA — Columbus
Traveler-Friendly Insight
Remote-first with structured travel: This Canonical role is explicitly work-from-home with only 2–4 weeks of global travel per year for events. That makes it highly compatible with a nomadic or location-flexible lifestyle — the vast majority of your year is location-independent.
Timezone flexibility: The job description does not specify fixed hours or a mandatory timezone overlap window. Canonical is well known for its distributed, async-friendly engineering culture, which means you likely have flexibility to structure your workday across various timezones. EMEA-based candidates are preferred, so staying within UTC-1 to UTC+5 is advisable for team collaboration.
Visa considerations for EMEA nomads: With an EMEA hint and no salary listed, here are practical options:
- Portugal (Lisbon/Porto): Portugal's Digital Nomad Visa (D8) suits remote workers with a foreign employer. Monthly cost of living ~€1,500–2,500 for a comfortable setup.
- Georgia (Tbilisi): EU/EEA citizens and many others can stay visa-free up to 365 days. Very low cost of living (~€800–1,200/month), excellent coworking scene.
- Estonia: EU member with a Digital Nomad Visa program; Tallinn is tech-friendly with solid infrastructure.
No equipment stipend or coworking allowance is explicitly mentioned, so budget for your own setup. The annual travel requirement (2–4 weeks) is a minor but pleasant perk — expect company-covered trips to summits and engineering sprints worldwide.
This is an exciting opportunity for a software engineer passionate about open source software, Linux, edge computing, and AI. Come build a rewarding, meaningful career working with the best and brightest people in technology at Canonical, a growing international software company.
Your role will be to help provide easy-to-deploy, secure, and customizable edge AI solutions, specifically focusing on Inference Snaps. You will work on designing and implementing silicon-optimized application packages for AI/ML inference, targeting devices from embedded systems to powerful gateways. Linux will be at the core of your everyday challenges.
The team values collaboration, peer reviews, good integration/unit testing, and writing maintainable code. General knowledge and experience with Linux as a developer platform is required.
What you'll do
- Collaborate proactively with a distributed team
- Develop and maintain open-source AI/ML inference application packages
- Debug issues and produce high-quality code to fix them
- Review code produced by upstream and/or other engineers at Canonical
- Discuss ideas and collaborate on finding good solutions
- Experiment with Ubuntu Core and showcase industry-grade Edge AI solutions
- Secure deployment of AI inference platforms on Linux
- Build optimized AI solutions for local inferencing on Ubuntu
- Work from home with global travel 2 to 4 weeks per year for internal and external events
Who you are
- Proven track record of at least 3 years of professional software development using Go or C++
- Experience working on and developing applications for Linux
- Interest and experience with a few of the following:
- Containers (Docker, Kubernetes)
- Snap, Debian, or RPM packaging
- Bash scripting
- CI/CD (GitHub Actions, GitLab CI, etc.)
- Local AI/ML inference frameworks
- Hardware accelerators: GPU, NPU
- Strong soft skills, communication and advocacy; passionate, enterprising, thoughtful, and self-motivated
- Bachelor's or equivalent degree in Computer Science, STEM, or similar field
Canonical is a growing international software company that works with the open-source community to deliver Ubuntu, the world's best free software platform. Our services help businesses worldwide reduce costs, improve efficiency, and enhance security with Ubuntu.
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.
Frequently Asked Questions
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