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      Entrevista para Principal AI Engineer

      27 may 2026
      Candidato de entrevista anónimo
      Londres, Inglaterra
      Sin ofertas
      Experiencia negativa
      Entrevista difícil

      Solicitud

      Acudí a una entrevista en Collinson (Londres, Inglaterra)

      Entrevista

      The interview process was among the most disappointing I’ve experienced in my 10+ year career. The first technical interview involved two interviewers; one of them, James, joined more than 15 minutes late due to an extended lunch break and never acknowledged the delay. During the interview, there were repeated side conversations and laughter with colleagues while I was answering technical questions, which came across as unprofessional. Afterwards, I was assigned a take-home assessment described as requiring “3–4 hours,” but in reality it involved well over 15 hours of work to build an end-to-end pipeline. I completed and submitted the assignment, then waited more than three weeks without any update. I also sent a polite follow-up email requesting feedback, but received no response at all. Looking through other reviews here, it seems that candidates being left without feedback may not be an isolated experience. Overall, the process felt very disrespectful of candidates’ time and effort.

      Preguntas de entrevista [1]

      Pregunta 1

      technical assessment reuqires “Boarding Pass OCR – Nearest Lounge & Destination Advisor” project. The task is to build an AI-powered travel assistant capable of extracting information from boarding pass images using OCR and providing contextual travel recommendations. The system should identify key fields such as the airport of departure, destination, terminal, gate, and flight number, while handling missing or low-confidence OCR outputs gracefully. Beyond extraction, the assistant should recommend nearby airport lounges and provide concise destination insights. Core requirements include accurate OCR parsing, terminal and gate handling, and robust processing of noisy or partially readable boarding passes. The project emphasises practical AI/ML engineering skills, including document understanding, information extraction, and recommendation logic, all within a realistic end-to-end pipeline scenario.
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