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Machine Learning Engineer Emploi Plein temps

il y a 3 semaines Engineering Zürich
Détails de l'annonce

About us: Daedalean is a Zürich-based startup founded by experienced engineers who want to completely revolutionize air travel within the next decade. We combine computer vision, deep learning, and robotics to develop full “level-5” autonomy for flying vehicles.
Your role:
Applying first certifiable Machine Learning models in the area of computer vision in an entirely new domain - aviation.

Your responsibilities will include the following:

    • Ensure that our neural networks perform strongly in all conditions, by combining work on data, model design, and training.
    • Ensure that this performance can be guaranteed, through careful design and verification activities, under our ML certification framework developed in collaboration with regulators.
    • Leverage transfer learning to massively increase the amount of training and evaluation data available, in particular through the use of simulation.

Preferred Qualifications and Experience:

    • Master’s or PhD degree in computer science, physics, mathematics or a related technical field.
    • Practical experience in deep learning for computer vision, ideally covering the whole stack from model architecture to the design and implementation of evaluation pipelines.
    • Proven research skills in industrial and/or academic environments, including the ability to work on difficult problems over long periods of time.
    • Excellent programming skills, including a system programming language such as C++ or Rust.

    • Experience in aerospace engineering or avionics is not required; we will teach you everything you need to know about the constraints of safety critical systems in airworthy applications.

Benefits:

    • A team of experienced engineers and researchers, who joined us from most recognized companies and institutions.
    • Difficult and interesting problems to solve.
    • Test flights in the Swiss Alps for you to join.
    • Pilot license subsidy.
    • Hybrid work setting.
    • Learning & Development budget: visit conferences of your choice.
    • Gym membership.