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      Colloquio per Senior AI Engineer

      17 dic 2025
      Dipendente anonimo
      Lubiana
      Offerta accettata
      Esperienza positiva
      Colloquio difficile

      Candidatura

      Ho presentato la mia candidatura tramite segnalazione di un dipendente. La procedura ha richiesto 3 settimane. Ho sostenuto un colloquio presso Bragg Group (Lubiana) nel mese di ago 2025

      Colloquio

      The entire interview process consisted of: 1. Online application; 2. HR screening online interview checking my professional background; 3. Call with technical hiring manager that focused on applying AI to building products and solving business problems; 4. Onsite with 3 interviews: 1) technical interview, 2) meet the team, 3) HR offer discussion. What stood out about this entire experience: 1. Streamlined. Unlike a lot of companies nowadays, the entire process from application to offer lasted 3 weeks. 2. High expertise. The interviews were hard, but they also showcased the high expert level in the company, which made me want to work here alongside the high performers. 3. Impact-focused. The interviews were not just checking technical/math knowledge. Instead, a lot of conversations were geared towards evaluating the business/product impact of the architectural/code decisions I proposed. 4. Geared towards success. Instead of asking "gotcha" questions, all of the interviews were set as "tell us about your experience with this [product area / technical challenge]" and then we drilled down from my answer. This allowed me to showcase my experiences, while also keeping the interviews challenging. 5. Team spirit. It was clear from the communication style online and then from the onsite, that this was a great team to join. Very fun, energetic, and open. The immediate connection was a very strong green light for joining the team.

      Domande di colloquio [3]

      Domanda 1

      How would you measure the success of a computer vision algorithm for real-time object detection in video?
      Rispondi alla domanda

      Domanda 2

      Design an end-to-end real-time inference pipeline that handles high-throughput ingestion, processes requests via an ML model, and publishes results to a downstream consumer.
      Rispondi alla domanda

      Domanda 3

      Design a recommender system to match players with games they will enjoy long-term, detailing how you would engineer features from raw gameplay telemetry and which specific metrics you would use to measure success beyond simple click-through rates.
      Rispondi alla domanda

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