Machine Learning Engineer

hace 2 semanas

Madrid, Madrid, España Veridas A tiempo completo
About the Role

We are seeking a highly skilled Machine Learning Engineer to join our team at Veridas, a leading company in the field of biometry and antispoofing technologies. As a Machine Learning Engineer, you will play a crucial role in developing and productizing cutting-edge biometry and antispoofing technologies based on neural networks.

Key Responsibilities
  • Develop and productize novel algorithms and solutions in antispoofing to bring state-of-the-art solutions from research to customers and end users.
  • Elaborate long-term plans and strategies in face biometrics and antispoofing to drive business growth and innovation.
  • Collaborate with cross-functional teams in the Technology and Operations areas to bring solutions from research to end users.
  • Participate in cutting-edge research in the areas of biometry, antispoofing, deepfakes, and generative AI to stay ahead of the competition.
Requirements
  • MSc or Engineering Degree in Computer Science, Telecommunication, or related field.
  • 3 years of experience developing Computer Vision or Machine Learning solutions.
  • 3 years of experience in SW development in Python or C++.
  • Experience working in small teams.
Preferred Qualifications
  • PhD in Machine Learning, Robotics, or related field.
  • 2 years of industry experience in the fields of biometry, object tracking, or generative AI.
  • Industry experience developing Computer Vision or Machine Learning technologies for edge devices (smartphones, FPGAs, custom HW).
  • Experience with MLOps frameworks.
Technical Skills
  • Python, Artificial Neural Networks, Computer Vision, Object Detection and/or classification.
  • C++, SCRUM experience.
Soft Skills
  • Excellent verbal and written communication skills.
  • Problem-solving skills and critical thinking.
  • Self-learning.
What We Offer
  • Fixed and variable remuneration, very competitive and in line with the candidate's experience.
  • Teleworking according to company policy.
  • Collaborative and multidisciplinary work environment.
  • Continuous training.

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