Framework Implementation: Implement and integrate ML programming framework on embedded platforms and multi-core architectures.
Integration: Integrate embedded applications and algorithm libraries both pre and post silicon into company software stack.
System Integration: Perform system-level integration and validation for heterogeneous platforms including DSPs and accelerators.
Algorithm Integration: Integrate state-of-the-art ML algorithms for automotive domain, such as driver identification, predictive maintenance,
Model Deployment: Deploy and integrate models on resource-constrained embedded systems.
Must have skills:
Processor Knowledge: Good knowledge of processor architecture and micro-architecture (e.g., SIMD/GPGPU/NEON).
Software Integration: 3+ years of hands-on experience in software integration and development on complex embedded computing platforms.
Mathematical Background: Good understanding of how algorithms work, such as image processing or neural networks.
Programming Languages: Proficiency in C and C++.
Any of the following are pluses:
Embedded ML Frameworks: Experience with frameworks like TensorFlow Lite, Glow, PyTorch Mobile, or ONNX Runtime. Understanding of model quantization and optimization techniques for embedded deployment.
DSP Algorithms: Prior work on DSP algorithms, neural networks kernel libraries or audio processing.
Standards Knowledge: Familiarity with Automotive SPICE, Functional Safety (ISO26262), and coding guidelines like MISRA or AUTOSAR.
Performance Optimization: Expertise in performance optimization on embedded platforms with ARM NEON or accelerator/DSP.
Hardware Knowledge: Background in electronic hardware or close to hardware level.
