Solicité el puesto a través de un captador. Acudí a una entrevista en Micron Technology (Singapur)
Entrevista
Recruiter reached out and then set up a interview with Hiring Manager who explained the team and Role. A logic test also had to be done before interview with Hiring Manager.
This was followed up by a take home assignment which was to be finished in 5 days. The assignment takes a lot of time if you want to actually build a fully automated solution.
On inquiring after few weeks HR said its negative. When asked for feedback on assignment the feedback was its very complex. As had spend a lot of effort on assignment atleast expected some feedback from the person who evaluated. Expecting people to slog on weekends and then not providing feedback is not a professional way to interview.
Preguntas de entrevista [1]
Pregunta 1
A MLOPS app expecting automation at every step possible
I had a phone interview with the recruiter, where they asked some basic questions. However, they informed me that I’m not eligible for that specific role, as it does not support any form of visa sponsorship.
Preguntas de entrevista [1]
Pregunta 1
Do you need any kind of sponser now or in the future
Solicité el puesto a través de la recomendación de un empleado. El proceso duró 1 semana. Acudí a una entrevista en Micron Technology (Taichung) en mar 2024
Entrevista
My interview process at Micron was conducted online through a video call for a Machine learning Engineering position with a specialization in computer vision. My interview was scheduled with the Technical manager and one senior engineer.
Preguntas de entrevista [1]
Pregunta 1
1. Give your introduction.
2. Questions about MS Project- Image Processing, what you do in
that project and what were your findings.
3. One question about, explaining the Ph.D. research work in short.
4. What kind of models are used in research, regression, or
classification?
5. What was the input/output dataset for your model?
6. What preprocessing technique did you apply to your data?
7. What is R2 score and why do we use it?
8. What is adjusted R2 score?
9. What is bias and variance?
10. What is overfitting and underfitting, explain them.
11. Techniques to overcome them.
12. Why you used RFR in your data, explain its working, what is
bagging and boosting?
13. What technique is used to determine the best hyperparameters?
14. What is cross validation and why it is used?
15. What is feature engineering?
16. One question about CNN project work, what layers I used and
why?
17. Which activation function is used in the output layer of CNN
classification problem?
18. Difference between Object classification and segmentation.
19. How can we convert spatial coordinates to image?
