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      NETGEAR

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      NETGEAR interviewsNETGEAR Senior Computer Vision Engineer interviewsNETGEAR interview


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      Senior Computer Vision Engineer Interview

      5 May 2018
      Anonymous interview candidate
      New York, NY
      Declined offer
      Negative experience
      Difficult interview

      Application

      I applied online. The process took 4 weeks. I interviewed at NETGEAR (New York, NY) in Mar 2018

      Interview

      The interview process is ambiguous, disorganized, and actively antagonistic towards candidates. The Arlo camera team seems to be in limbo regarding what direction to go in, and the office environment's very dirty and sloppy state seems to be a reflection of the sloppy leadership and confused direction for the Arlo team. - 3 rounds of technically probing, in-depth phone interviews with a mix of questions about computer vision, machine learning, and standard software development. No feedback between interview rounds, no contact from a recruiter or hiring manager ... just impromptu invitations coming out of the blue to do more and more phone interviews. - The on-site interview was also ambiguous, with no printed schedule or itinerary. Several employees remarked to me, "Boy it looks like you'll be here all afternoon," during my interview, but I had no idea what the schedule really was at any point. No one was there to greet me either. I literally wandered unguided off the elevator and eventually just interrupted some random engineers at their desks to ask where I should go. Start to finish it lasted almost 6 hours, from 12:30 pm until 6:15 pm, and I was given no schedule or information at any point. - Some on-site interview sessions were conducted remotely over video chat, but the company seemed incredibly disorganized, video calls were dropping, constant audio problems, and the only free room for me to sit in was the messy office of an employee who happened to be working remotely that day. Given that some of the questions were technical in nature, they certainly didn't offer a situation where someone could be comfortable or have resources like paper and a pencil to work through problems. - The office kitchen was literally disgusting. The sink was completely full of dirty dishes, there were plates with old food on them sitting on the table, food and candy wrappers everywhere. It looked like they try to save money by either never hiring a cleaning staff or only hiring them to come in once per month. I know this sounds like a weird issue to report about an interview, but I'm telling you it was so disgusting that it was actually disruptive for the whole day, and left me with an instant impression that they don't care about their work space. - Some of the technical interview sessions were downright rude. One interviewer literally brought in printed sheets of multiple choice machine learning questions and just slid the papers across the table to me and asked me to figure them out, no discussion, like a college exam. I actually was able to answer them all with a good level of detail, only to be given more sheets of questions for almost 90 minutes! After the machine learning questions ended, then there were two sheets of really juvenile trivia questions regarding how to use the pandas library in Python for common join / merge / filtering operations like for data summaries or data cleaning. The whole demeanor of the interview was shocking-- no communication, no discussion about problem solving. Just complete silence while filling out sheet after sheet of multiple choice questions. I was not given the chance to ask any questions about the role or the company. I have never felt so close to getting up and just walking out of an interview in all my life. - Finally, at the end of the day, the team manager sat with me and basically told me that the job they were hiring for would not really involve machine learning for computer vision (even though this was the bulk of all the technical questions I was asked about all day). It felt like a total bait and switch to get an overqualified machine learning expert to work on simplistic computer vision systems and to focus more on data engineering and Docker-based deployments than to focus on real machine learning. - After the interview was over I was given an informal verbal offer over the phone. But when I discussed the salary level, private working space, benefits, and other compensation that I would be seeking (all in line with my job history and levels which are competitive and commonplace for senior level computer vision roles), the hiring manager simply never contacted me again. Deeply, deeply unprofessional. Overall my impression is that the Arlo team is in disarray. They don't know what they need and they aren't willing to pay competitive compensation for skilled workers. Despite this, they will still rake you over the coals with an incredibly intense, sloppy, and unfriendly interview process. My advice: steer clear. This place seems really unprofessional, the workers on the team don't seem happy or clear about what the team goals even are, and they seem like they really might be trying to bait and switch someone overqualified to join while not offering good work projects. I'm not even sure they have enough self-awareness to understand how antagonistic and unfriendly their interview process is.

      Interview questions [1]

      Question 1

      - Write a function to produce the convolution of an image with a filter - Write a function that fills a buffer with a rolling set of image object segmentation results - From a set of loss functions like L2 loss or hinge loss, explain which ones are rotationally invariant, sensitive to rescalings of the input features, and which are convex. - Given a set of standard random forest trees and a set of gradient boosted trees, explain how the model's predictions would change if you remove the first tree, the last tree, or make other changes after training. - Talk about a system design you've worked on where a web service provides computer vision computations on input images. How did you test it? How did you deploy it? - Given a pandas DataFrame with some numeric columns, how to produce counts of data for different category IDs, including with or without extra processing to remove noisy, missing, or outlier data points.
      Answer question
      4

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