Ir para o conteúdoIr para a pasta
  • Vagas
  • Empresas
  • Salários
  • Para empresas

      Avance em sua carreira

      Descubra qual pode ser seu salário, conquiste a vaga dos seus sonhos e compartilhe insights de qualidade de vida com sigilo.

      employer cover photo
      employer logo
      employer logo

      Radix.ai

      Empresa engajada

      Sobre
      Avaliações
      Remuneração e benefícios
      Vagas
      Entrevistas
      Entrevistas
      Buscas relacionadas: Avaliações da empresa Radix.ai | Vagas da empresa Radix.ai | Salários da empresa Radix.ai | Benefícios da empresa Radix.ai
      Entrevistas da empresa Radix.aiEntrevistas do cargo de Machine Learning Engineer da empresa Radix.aiEntrevista da empresa Radix.ai


      Glassdoor

      • Sobre
      • Prêmios
      • Blog
      • Fale conosco

      Empresas

      • Conta gratuita de empresa
      • Área da empresa
      • Blog para empresas

      Informações

      • Ajuda
      • Regras da Comunidade
      • Termos de Uso
      • Privacidade e opções de anúncios
      • Não venda nem compartilhe minhas informações
      • Ferramenta de consentimento de uso de cookies

      Trabalhe conosco

      • Anunciantes
      • Carreiras
      Baixe o aplicativo:

      • Busque por:
      • Empresas
      • Vagas
      • Localizações

      Copyright © 2008-2026. Glassdoor LLC. “Glassdoor”, “Worklife Pro”, “Bowls” e o logotipo do Glassdoor são marcas comerciais pertencentes à Glassdoor LLC.

      Empresas seguidas

      Fique por dentro de todas as oportunidades e dicas internas seguindo as empresas de seus sonhos.

      Buscas de vagas

      Comece a buscar vagas para receber atualizações e recomendações personalizadas.

      As melhores empresas na categoria “Remuneração e benefícios” perto de você

      avatar
      Deloitte
      3.5★Remuneração e benefícios
      avatar
      KPMG
      3.6★Remuneração e benefícios
      avatar
      bp
      3.9★Remuneração e benefícios
      avatar
      SLB
      3.9★Remuneração e benefícios

      Entrevista para Machine Learning Engineer

      25 de jun. de 2020
      Funcionário(a) sigiloso(a)
      Bruxelas

      Outras avaliações de entrevista de vagas de Machine Learning Engineer da empresa Radix.ai

      Entrevista para Machine Learning Engineer

      8 de abr. de 2024
      Candidato(a) sigiloso(a) à entrevista
      Nenhuma oferta
      Experiência positiva
      Entrevista com nível médio de dificuldade
      Oferta aceita
      Experiência positiva
      Entrevista difícil

      Candidatura

      Candidatei-me online. O processo levou 1 semana. Fui entrevistado pela Radix.ai (Bruxelas) em abr. de 2020

      Entrevista

      The process is long, but really worth it. You first have to pass a small Kaggle competition and they will get in touch with you if you score well. Then, you have to pass two interviews: one technical and one business. They ask increasingly harder questions to push you to your limits and evaluate your potential. I had a really great experience with Radix, people there are really smart and friendly. Would definitely recommend!

      Perguntas de entrevista [1]

      Pergunta 1

      Business case: the interviewer plays the client and you have to ask him relevant questions
      Responder à pergunta
      4

      Candidatura

      O processo levou 3 semanas. Fiz uma entrevista na empresa Radix.ai.

      Entrevista

      Great interview process, very structured and provided relevant feedback when explaining why I was declined. 1st: call with head of delivery to talk about motivations and Radix 2nd: take-home assignment available on their github account. 3rd: 1-to-1 technical interview

      Perguntas de entrevista [3]

      Pergunta 1

      What is k-means ? What is DBSCAN ? What is the advantage of DBSCAN over K-Means ?
      Responder à pergunta

      Pergunta 2

      What is a diffusion model ? How does it work ?
      Responder à pergunta

      Pergunta 3

      What is object detection ? What is object segmentation ?
      Responder à pergunta

      Entrevista para Machine Learning Engineer

      10 de set. de 2021
      Candidato(a) sigiloso(a) à entrevista
      Nenhuma oferta
      Experiência neutra
      Entrevista com nível médio de dificuldade

      Candidatura

      Candidatei-me online. O processo levou 4 semanas. Fui entrevistado pela Radix.ai em ago. de 2021

      Entrevista

      The recruitment process is very transparent. You get to know from the beginning that there are 3 steps - HR Interview, Technical Takeaway + Interview, and Business Case Interview. The HR interview is an introductory one, with a few behavioural questions to see if you fit into the culture. My interviewer was very friendly and gave me good overall vibes. Following, I received the instructions for the takeaway assignment, which are also public on their website. I worked for about a week and a half on the assignment, in the end scoring more than enough to proceed. During this step, I asked one of their employees for support as I had an issue, and he offered his help without a second thought. Very nice! After submitting my solution, I had a technical interview with their tech lead, in which I explained my solution with bad and good, and he also asked me some very general machine learning questions. I was told I performed well and my technical knowledge was excellent. After the technical interview, I got an invitation to another interview, but contrary to my expectations, it was not the final step, but a call to tell me that I was rejected. The main reason was me still being a (final year) student, so they did not think combining with a full-time job was possible, and me not being committed to living in Belgium after I finished my studies. The end was a bit bitter, I would have liked to know about the incompatibility from the beginning, or even sooner, but in the rest the process was enjoyable and I ended up learning a lot.

      Perguntas de entrevista [1]

      Pergunta 1

      What's the difference between object detection and instance segmentation, what's the difference between kmeans and dbscan, etc.
      Responder à pergunta
      1