Applied ML control engineer

Requirements:
M.Sc.in Mechatronics/Autonomous Systems/Robotics/Mechanical Engineering/Aeronautical Engineering or similar.
Demonstrated experience with data driven algorithmics, advantage if applied to real world time series data (like audio, accelerometer etc.). Solid knowledge in Control Theory.
Solid knowledge in (at least two):
o Neural Networks Architectures (RNN/CNN/PINN), o Hyperparameters Optimization, o Feature Engineering, o Optimization Methods, o Reinforcement Learning o Real-Time Learning/Estimation Programming skills (at least two): C / C ++, Python, MATLAB / Simulink. Ability to design algorithms from scratch as well as to use open-source libraries. Personal skills - very high analytical skills, good communication and interaction with colleagues & customers, independence & quick learning. Fluent in English, both writing and speaking.
Advantages: o PhD in related field, o Experience with ML infrastructure, edge deployment, o Experience in programming DSP peripherals, o Relevant experience in the motion control industry.