Ho presentato la mia candidatura online. Ho sostenuto un colloquio presso AltaML
Colloquio
It was only 1 interview with 3 people. It included behavioural questions, and technical questions. The interviewers were really friendly and welcoming. The questions level was very reasonable and they care more about how you analyze any problems and provide some initial sensible solutions.
Domande di colloquio [1]
Domanda 1
A question about a ML project you worked on before, the problem and your approach to solve it
Ho presentato la mia candidatura tramite l'università. La procedura ha richiesto 2 settimane. Ho sostenuto un colloquio presso AltaML (Calgary, AB) nel mese di lug 2022
Colloquio
Interview consisted of an online coding assessment using Python and a behavioral interview with a team lead and HR manager involving different personal scenarios and example situations. Mostly questions about previous experiences.
Domande di colloquio [1]
Domanda 1
Describe a time where a colleague was not pulling their weight. What did you do?
Ho presentato la mia candidatura tramite un selezionatore. Ho sostenuto un colloquio presso AltaML (Calgary, AB) nel mese di gen 2025
Colloquio
I recently interviewed for a Machine Learning Developer position at AltaML and had a great experience overall. The interview process was well-structured and included four steps: an initial HR call, a hands-on ML project, a team interview, and a final technical challenge focused on solving problems using LLMs and Retrieval-Augmented Generation (RAG). The team was incredibly supportive and friendly throughout, which made the process smooth and enjoyable. I was excited to receive a job offer at the end, which validated my skills and effort. However, I ultimately decided to decline the offer because the salary didn’t meet my expectations. While the process was a bit time-intensive, especially with the project component, it was a valuable learning experience and gave me exposure to practical, cutting-edge AI applications.
Domande di colloquio [1]
Domanda 1
Solving problems using LLMs and Retrieval-Augmented Generation (RAG)