Canonical AI Engineer interview typically runs 4 rounds: written interview, Thomas cognitive test, behavioral interview, technical interview. The process took several stages and felt unusually long and school-focused.
$102K
Avg. Base Comp
$109K
Avg. Total Comp
4
Typical Rounds
3-6 weeks
Process Length
Our candidates report that Canonical’s AI Engineer process puts a surprising amount of weight on background signals before it ever gets close to day-to-day AI work. One experience described questions about high school ranking, home language, what kind of student they were, and what classmates would remember them for — alongside the more expected inventory of Python, cloud operations, operating systems, and open source exposure. That mix tells us the company is looking for more than technical fluency; it seems to care about how candidates present themselves as learners and problem-solvers over time.
A recurring theme is that Canonical appears to screen for breadth and self-directed familiarity rather than a narrow model-building specialty. The same candidate was asked what AI tools they use, why they wanted Canonical, and which Canonical or open source tools they already knew, which suggests they want people who can connect their own experience to the ecosystem Canonical lives in. We’ve also seen the process feel more like a filter than a conversation, especially with the Thomas cognitive test layered in. The non-obvious takeaway is that candidates who expect a role-specific technical discussion may be caught off guard; the stronger signal here is whether you can show a credible, well-rounded history with software, Linux-style environments, and open source work without sounding rehearsed.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Canonical process.
The process felt unusually long and very school-focused for an AI Engineer role. It started with a written interview that had a lot of questions in English, and many of them were about my background rather than the job itself. I had to answer things like how I ranked in math and my home language in my final year of high school, what kind of student I was, what my classmates would remember me for, and what I did outside of class. There were also questions about my software experience, including which operating systems, development environments, languages, and databases I had used, plus how I would rate my Python skills and my experience with public cloud operations and large-scale estate management. After that came a Thomas cognitive test with five parts, which added to the feeling that the process was more about screening than actually discussing the role.
The later stages were described to me as separate behavioral and technical interviews, and they seemed to be independent rather than building on each other. In the written portion, I also saw questions like what AI tools I use, why I wanted Canonical, what I had used from Canonical before, and which open source tools I knew. Overall, it was a tedious process and I did not get any useful feedback after being rejected. What stood out most was how much emphasis there was on school history and general background compared with actual AI engineering work. If you go into it, be ready for a long written screen, a cognitive test, and very broad questions about your academic record, Python, cloud experience, and open source familiarity.
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Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
The process begins with a long written interview focused heavily on background and screening questions. Candidates answer in English about their school history, academic ranking, home language, extracurriculars, software experience, Python skill level, cloud operations experience, and familiarity with open source and Canonical tools.
After the written screen, candidates complete a Thomas cognitive assessment with five parts. The experience described this as a separate screening step rather than a role-specific technical discussion.
Later stages include a behavioral interview focused on general fit and background. The interview experience suggests this round is separate from the technical interview and does not necessarily build directly on the earlier written screening.
A separate technical interview follows, though the candidate noted it felt independent from the behavioral stage. Based on the written screening, this likely touches on Python, cloud operations, large-scale estate management, AI tools, and open source experience.