Skip to contentSkip to footer
  • Community
  • Jobs
  • Companies
  • Salaries
  • For employers
      Notifications

      Loading...

      Elevate your career

      Discover your earning potential, land dream jobs, and share work-life insights anonymously.

      employer cover photo
      employer logo
      employer logo

      ITHAKA

      Engaged employer

      About
      Reviews
      Pay and benefits
      Jobs
      Interviews
      Interviews
      Related searches: ITHAKA reviews | ITHAKA jobs | ITHAKA salaries | ITHAKA benefits
      ITHAKA interviewsITHAKA Principal Machine Learning Engineer interviewsITHAKA interview


      Glassdoor

      • About / Press
      • Awards
      • Blog
      • Research
      • Contact Us
      • Guides

      Employers

      • Free Employer Account
      • Employer Centre
      • Employers Blog

      Information

      • Help
      • Guidelines
      • Terms of Use
      • Privacy and Ad Choices
      • Do Not Sell Or Share My Information
      • Cookie Consent Tool
      • Security

      Work With Us

      • Advertisers
      • Careers
      Download the App

      • Browse by:
      • Companies
      • Jobs
      • Locations
      • Communities
      • Recent posts

      Copyright © 2008-2026. Glassdoor LLC. "Glassdoor," "Worklife Pro," "Bowls" and logo are proprietary trademarks of Glassdoor LLC.

      Company Bowl sample

      Want the inside scoop on your own company?

      Check out your Company Bowl for anonymous work chats.

      Bowls

      Get actionable career advice tailored to you by joining more bowls.

      Followed companies

      Stay ahead in opportunities and insider tips by following your dream companies.

      Job searches

      Get personalised job recommendations and updates by starting your searches.

      Top companies for "Compensation and Benefits" near you

      avatar
      Amazon
      3.7★Compensation and benefits
      avatar
      Google
      4.5★Compensation and benefits
      avatar
      Shopify
      3.6★Compensation and benefits
      avatar
      Amazon Web Services
      3.9★Compensation and benefits

      Principal Machine Learning Engineer Interview

      Aug 19, 2024
      Anonymous interview candidate
      No offer
      Positive experience
      Difficult interview

      Application

      I applied online. The process took 4 weeks. I interviewed at ITHAKA

      Interview

      It was extensive, as fits the role. There were a total of five rounds of interviews. The first round was straightforward. It was a 30 minute, 3 question one-way interview designed to screen candidates for machine learning expertise. The second round was an interview with the hiring manager; the head of data science. This interview was designed to get to know the candidate's problem solving method and dig deeply into their expertise. The third round was an interview with the CTO, and was designed as more of a vibe check and culture fit. We just chatted for a while, talked about the company, the position, and the location. All in all it was a fun time. The fourth round was an interview with the technical team, the people who were currently working with ML at ITHAKA. It was designated a technical interview, but was mostly an interview about management style. The engineers asked about how I would solve various management problems, as well as ascertained my technical skill. The final interview was with every department head, to see how I meshed with my "indirect colleagues" so to speak. It was a brutal slog, and I did not do well at addressing their concerns. If I were to have this interview again, I would address their concerns more directly and advocate for myself more, even if I had to be confrontational to do so. The worst part of the interview process was the gender disparity. Of the eleven people who interviewed me, all eleven were men.

      Interview questions [1]

      Question 1

      We have an extensive collection of digitized archive records. Sometimes the OCR technologies that digitize these physical records make mistakes and produce some sections of garbled text. If you were to train an LLM on these records, how would you handle such OCR errors affecting training and inference?
      1 Answer