Computer Vision Intern applicants have rated the interview process at ZEISS Group with 3.5 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 50% positive. To compare, the company-average is 52.7% positive. This is according to Glassdoor user ratings.
Candidates applying for Computer Vision Intern roles take an average of 4 days to get hired, when considering 2 user submitted interviews for this role. To compare, the hiring process at ZEISS Group overall takes an average of 41 days.
Common stages of the interview process at ZEISS Group as a Computer Vision Intern according to 2 Glassdoor interviews include:
Skills test: 33%
Phone interview: 17%
One on one interview: 17%
Group panel interview: 17%
Background check: 17%
Here are the most commonly searched roles for interview reports -
I applied online. The process took 4 days. I interviewed at ZEISS Group (Jena) in May 2023
Interview
I was called for interview and then they asked me question related to my Cv, previous internships which were correlated to this position for 15 minutes and then majority of their focus was towards Deep Learning question and the interview lasted for 45 min
Interview questions [1]
Question 1
Given a situation of their project and asked me what could be the best possible solution
I applied online. I interviewed at ZEISS Group (Dublin, CA)
Interview
In the interview process, I initially went through a resume screening phase, where my qualifications and experience were evaluated. Following the screening, I was invited to a technical interview. During this interview, I was asked a variety of questions to assess my technical skills and problem-solving abilities. The interviewer focused on topics relevant to the role, such as programming concepts, algorithms, and my past experience in related projects. I was also given practical problems to solve, which allowed me to demonstrate my technical proficiency in real-time. Overall, the interview process was designed to evaluate both my technical knowledge and my ability to apply it effectively.
Interview questions [1]
Question 1
Can you explain how you would approach denoising an image using machine learning techniques?