Our fast-growing team is seeking a talented scientist to conduct original research to demonstrate the use of machine learning in safety-critical systems. Your research areas will include
- Interpretability: demonstrating what is going on inside our neural networks
- Correctness: verifying that systems produce the desired output under all necessary conditions
- Generalization: learning accurate and efficient representations to mimic higher-level cognitive functions
- Robustness: ensuring the system is robust to varying inputs, unseen data and adversarial attacks
Your role will collaborate closely with other engineers, researchers and pilots on the team to quickly validate your ideas in the lab and in the air
We work closely with academic institutions and expect you to actively contribute to the research community through publications and conference visits (ICML, NeurIPS, CVPR, …).
Preferred Qualifications and Experience:
- Master’s or PhD degree in computer science, physics, mathematics, a related technical field or equivalent practical experience
- Experience in machine learning theory (statistical learning, linear algebra, calculus, ...)
- Proven research skills in industrial and/or academic environments
- Excellent programming skills
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.
- Combine the best of both worlds: a) work in fast-growing startup and b) collaborate with and learn from very experienced engineers and scientists that have previously worked at Google, SpaceX, CERN, Imperial College, and ETH Zürich.
- Build cutting-edge technology that will shape our future.
- Join our pilots to test your ideas in the air during test flights in the Swiss Alps.
- Develop scarce and marketable skills in machine learning, computer vision and robotics that are relevant beyond aviation (e.g., autonomous driving, medical applications, pharmaceuticals)
How to Apply:
Send your Resume/CV, including your portfolio of projects to email@example.com
. Tell us a bit about yourself, why you think you are a good fit for us and why we are a good fit for you.