About Us
Daedalean is a Zürich-based startup founded by experienced engineers dedicated to revolutionizing air travel within the next decade. We leverage advanced technologies such as computer vision, deep learning, and robotics to develop full “level-5” autonomy for flying vehicles.
Your Role
You will be instrumental in applying the first certifiable Machine Learning models in the area of computer vision within the aviation industry. Your expertise will contribute significantly to our mission.
Your Responsibilities
- Ensure that our neural networks operate effectively in all conditions by working on data, model design, and training.
- Guarantee performance through careful design and verification activities, in alignment with our ML certification framework created in partnership with regulators.
- Utilize transfer learning to significantly enhance the quantity of training and evaluation data available, particularly 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 encompassing the entire stack from model architecture to evaluation pipeline design and implementation.
- Demonstrated research skills in both industrial and academic environments, with the ability to tackle challenging problems over extended periods.
- Excellent programming skills, including proficiency in a system programming language such as C++ or Rust.
Experience in aerospace engineering or avionics is not necessary; we are committed to providing you with the knowledge required to navigate safety-critical systems in airworthy applications.
Benefits
- A team of seasoned engineers and researchers from highly recognized companies and institutions.
- A challenging array of interesting problems to solve.
- Opportunities to participate in test flights in the Swiss Alps.
- Pilot license subsidy.
- A hybrid work environment.
- Learning & Development budget for attending conferences of your choice.
- Gym membership.
Apply online using the form below. Only applications matching the job profile will be considered.