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      Entretiens chez ITHAKAEntretiens d’embauche pour Principal Machine Learning Engineer chez ITHAKAEntretien chez ITHAKA


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      Entretien pour Principal Machine Learning Engineer

      19 août 2024
      Candidat à l'entretien anonyme
      Aucune offre
      Expérience positive
      Entretien difficile

      Candidature

      J'ai postulé en ligne. Le processus a pris 4 semaines. J'ai passé un entretien chez ITHAKA

      Entretien

      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.

      Questions d'entretien [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 réponse

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