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Software Engineer – Edge AI

AI Summary
Canonical is seeking a software engineer to build and maintain open-source Edge AI inference packages for Ubuntu and Linux-based devices. The role focuses on silicon-optimized AI/ML inference applications, secure deployment, debugging, code reviews, and packaging technologies such as Snap, Debian, or RPM. Candidates need at least three years of professional Go or C++ development experience and strong Linux application-development knowledge. The engineer will collaborate with a distributed team, contribute to CI/CD and testing practices, and occasionally travel globally for events. This is a technically focused remote role for EMEA-based engineers interested in open source, embedded-to-gateway edge computing, and hardware acceleration.
Remote from
EMEA
Salary
Undisclosed
Employment
Full Time
Experience
Open level
Published
Apply before
7 Dec 2026
Listing views
29
Application actions
2
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Opportunity details

About this role.

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 us 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.

In our team we value 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 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
  • You have 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
  • You value soft skills, communication and advocacy, and are passionate, enterprising, thoughtful, and self-motivated
  • You have a Bachelor’s or equivalent in Computer Science, STEM or similar degree

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.

#LI-remote

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe position requires production-level Go or C++, Linux expertise, AI inference deployment knowledge, and optimization across varied edge hardware. Engineers must independently troubleshoot complex issues while participating actively in distributed reviews and technical discussions.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$85,000
US market range (per year)$65k–$110k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation is disclosed. The yearly period and USD estimates are inferred for a mid-level Edge AI/Linux software engineering role in the EMEA remote market; actual pay will vary by hiring country, employment arrangement, and experience.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you package and distribute an AI inference application for Ubuntu-based edge devices?

I would first identify runtime dependencies, model assets, hardware requirements, and security constraints. I would create a reproducible package, preferably using Snap where confinement and transactional updates are beneficial, then validate installation and operation across the supported device architectures. I would automate builds and tests in CI and document configuration, model updates, and rollback procedures.

Describe how you would diagnose an inference-performance problem on a Linux edge gateway.

I would establish a baseline for latency, throughput, memory use, CPU utilization, and accelerator utilization. I would profile the application and runtime, verify model format and accelerator delegation, inspect logs and system resource contention, and compare results with a controlled reference environment. Based on the findings, I would optimize the bottleneck and add regression benchmarks to prevent recurrence.

What considerations are important when deploying AI inference securely on edge devices?

Key considerations include minimizing the software footprint, using signed and verified packages, applying least-privilege permissions, isolating workloads, protecting model and credential assets, and maintaining a reliable update path. I would also ensure that logs do not expose sensitive data and that vulnerabilities in dependencies can be tracked and patched quickly.

How do you approach code reviews in a distributed open-source engineering team?

I focus reviews on correctness, maintainability, security, testing, and consistency with project conventions. I provide specific and respectful feedback, explain the reasoning behind requested changes, and distinguish blocking concerns from optional suggestions. I also treat reviews as a collaborative learning process and make sure my own changes are small, well-tested, and clearly documented.

Tell us about a time you resolved a difficult Linux application issue.

In a typical approach, I would reproduce the issue in a controlled environment, collect application and system-level diagnostics, and reduce the failure to the smallest reproducible case. I would isolate whether the root cause was in the application, dependency, packaging, kernel interface, or device configuration, implement and test a focused fix, and document the outcome. I would then add automated coverage or monitoring to catch similar failures earlier.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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