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      Entrevistas en WelocalizeEntrevistas para el cargo de Senior AI/ML Engineer en WelocalizeEntrevista en Welocalize


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      Entrevista para Senior AI/ML Engineer

      19 feb 2026
      Empleado anónimo
      Noida
      Oferta aceptada
      Experiencia positiva
      Entrevista difícil

      Solicitud

      Me postulé a través de un reclutador. Acudí a una entrevista en Welocalize (Noida) en feb 2026

      Entrevista

      2 Rounds as of now - Both Technical 1st case a basic case of questions and DSA type of question discussion 2nd was a more difficult one, on the basics, scenario based

      Preguntas de entrevista [2]

      Pregunta 1

      1. Asked to implement a simple Bag of Words vectorizer from scratch using only the Python standard library. 1.a. Over the BOW -> I created a function with '_' as starting - The interviewer asked why did I start it with '_'? 1.b. So what are we doing with the words that are not in our vocabulary (let’s say we wanna know we we are trying to factorise sentence that contains some words that are not in our purpose or vocabulary) 1.c. Lets say the vocab is growing to millions - How would you reduce the vocabulary size in this case, which is gonna be dimensional? 1.d. In the output of my vector - you can see where we can see that most of the elements in vectors are zero what does that signify? 1.e. Is this class CPU or GPU bound? 2. Spiral Matrix Leetcode - discussion and scenarios
      1 respuesta

      Pregunta 2

      Interview Round 2: 1. Project related questions and scenario based over that - Problems I faced and scenarios 2. What are unit tests and what are its uses? 3. Caching, what it introduces and why is it used? 4. Why people favour OOPs over functional 5. Precision vs recall (meaning and also scenario based where will I prefer what) 6. What do you understand by embeddings and why do we use them, do we use them in images and videos? 7. Lets say you are building a ML model and there is overfitting , what does it mean? what do you do and how do you reduce it? 8. Imbalanced data, what do you do? 9. Lets say GPT has memorised everything and it answers everything so overfitting helps us right? - Trick question
      Responder pregunta