CS27 BAC+5 - Étude comparative de frameworks d'optimisation IA (H/F)
Core
Comparative study of AI trajectory prediction model deployment frameworks on embedded automotive architectures to evaluate precision, inference time, and resource consumption.
Role type
Intern, embedded systems & AI deployment optimization
Builds
Embedded software for advanced driver assistance systems (ADAS)
Domain
Automotive industry, embedded systems, machine learning deployment
Deliverable
production ML models
Required skills
C/C++ development, version control (Git), machine learning fundamentals, path planning knowledge
Preferred skills
Embedded systems experience, model optimization techniques
Technologies
TorchScript, ONNX Runtime, TensorRT, C++
Responsibilities
Compare framework-based vs manual C++ re-encoding approaches for model deployment; evaluate impact on inference precision and latency; analyze resource usage on embedded hardware
Seniority
Intern, BAC+5 level
