Ho presentato la mia candidatura online. La procedura ha richiesto 3 settimane. Ho sostenuto un colloquio presso AMD
Offerta accettata
Esperienza positiva
Colloquio facile
Candidatura
Ho presentato la mia candidatura tramite segnalazione di un dipendente. La procedura ha richiesto una settimana. Ho sostenuto un colloquio presso AMD (Austin, TX)
Colloquio
The whole interview process was very smooth. The interviewer started off with the basics, with him delving deeper into the topic, every time you answer correctly. He helped me too when I got stuck, so I was able to answer most of the questions I didn't know the answers of with the hints he provided. He summed up the interview by connecting all the dots at the end, and this gave a much clearer picture of the work I am supposed to do during the intern.
Domande di colloquio [1]
Domanda 1
Questions on Electromigration, Thermal inversion, SCMOS, Pass transistor logic, and lots of grilling on Setup,hold (which specific corner, what is supposed to be checked), how to resolve them,etc.
30 minutes of resume screening going in depth on what is on your resume and your skills, then an online assessment, then 3 rounds of a mixture of technical and behavioral (each 30 minutes).
Ho presentato la mia candidatura tramite segnalazione di un dipendente. Ho sostenuto un colloquio presso AMD (Austin, TX) nel mese di ago 2020
Colloquio
Applied through referrals (position is actually postdoc fellow in machine learning). Talked to the manager and then had a phone screen with a senior researcher. Answered all questions and they seemed interested, but they are way too slow and if you don't follow up, they won't get back to you at all. The senior researcher seemed nice but mentioned doesn't have enough experience in machine learning. I don't know what's the point of interviewing postdoc applicants in the machine learning area and wasting their time, if you can't even find an appropriate interviewer who has enough expertise!
Domande di colloquio [1]
Domanda 1
Basic ML and deep learning questions (regularization, different types of gradient descent and their drawbacks), etc.