Machine Learning Engineer
Vor 7 Tagen
Role: Machine Learning Engineer
Location : Zürich, Switzerland (On-site)
Flight Software, Computer Vision and AI /
Mode of Hiring : Full-Time
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:
o Ensure that our neural networks perform strongly in all conditions, by combining work on data, model design, and training.
o Ensure that this performance can be guaranteed, through careful design and verification activities, under our ML certification framework developed in collaboration with regulators.
o 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:
o Master's or PhD degree in computer science, physics, mathematics or a related technical field.
o Practical experience in deep learning for computer vision, ideally covering the whole stack from model architecture to the design and implementation of evaluation pipelines.
o Proven research skills in industrial and/or academic environments, including the ability to work on difficult problems over long periods of time.
o 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:
o A team of experienced engineers and researchers, who joined us from most recognized companies and institutions.
o Difficult and interesting problems to solve.
o Test flights in the Swiss Alps for you to join.
o Pilot license subsidy.
o Hybrid work setting.
o Learning & Development budget: visit conferences of your choice.
o Gym membership.
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