In this role, you will: (1) Research and develop Generative AI and deep learning-based systems for in-vehicle domains — Infotainment, Speech, UI, and multimodal ADAS — with a focus on architectures deployable on resource-constrained edge hardware. (2) Lead integration of GenAI frameworks and toolchains optimized for automotive edge compute, driving adoption of cutting-edge on-device AI techniques from research into production. (5) Track industry and academic advances in edge AI and efficient LLM techniques, translating relevant breakthroughs into practical, deployable improvements for in-vehicle systems. Deep hands-on experience with ML/DL frameworks and tooling: TensorFlow, PyTorch, NeMo, TAO, TensorRT, CUDA, and vLLM, with working knowledge of edge-inference frameworks (e.g., TensorRT-LLM, ONNX Runtime, GGUF/llama.cpp). Working knowledge of in-vehicle AI technical stacks — Speech, Voice, or ADAS/AD systems.
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