The role involves designing end-to-end differentiable systems that jointly optimize sensor control, illumination patterns, and perception tasks for greener mobility
Job Summary
The role involves designing end-to-end differentiable systems that jointly optimize sensor control, illumination patterns, and perception tasks for greener mobility.
Candidates will develop domain adaptation techniques enabling synthetic-to-real transfer without retraining while balancing perception quality with power constraints.
Join a team of over 20,000 engineers in a multi-cultural environment committed to limiting environmental impact and recognized as a leader in sustainable automotive development.
Matching Summary
The role involves designing end-to-end differentiable systems that jointly optimize sensor control, illumination patterns, and perception tasks for greener mobility.
Skills & Requirements
Must-have
Deep learning model development
Domain adaptation techniques
Embedded AI model optimization
Real-world vehicle testing validation
Python C++ CUDA programming
Nice-to-have
Research publication at top conferences
Multi-cultural collaboration experience
Sustainable mobility focus
3D rendering engine familiarity
Key Requirements
Bachelor's or Master's degree in CS or related field
3+ years developing deep learning and reinforcement learning models
Expert-level PyTorch/TensorFlow with custom architecture design
Hands-on experience with automotive sensors and datasets