Edge Models Optimization Research Engineer
Pubblicato il 21-10-2026 - Huawei Italia in Trento
Questa posizione è in Huawei Italia Il processo di selezione sarà interamente gestito Huawei Italia. -- Position Summary: Huawei’s Pisa Research Institute is a core innovation hub in the field of automotive software and embedded systems. We are looking for a highly motivated researcher to join our AI platform team and contribute to the development of next-generation models for intelligent vehicles and edge computing systems. The successful candidate will focus on post-training techniques, efficient Mixture-of-Experts (MoE) architectures, and deployment optimization on resource-constrained platforms. Working closely with system software, compiler, and hardware teams, the candidate will help bridge cutting-edge AI research and real-world automotive applications. Key Responsibilities: * Investigate advanced post-training approaches for large language models and multimodal foundation models, including supervised fine-tuning, preference alignment, reinforcement learning-based optimization, and knowledge distillation.
* Explore efficient MoE architectures and sparse inference mechanisms that enable high-quality intelligence under strict latency, memory, and power constraints. * Develop and evaluate model compression techniques such as quantization, pruning, and low-rank adaptation, while also optimizing inference performance across heterogeneous computing platforms including CPUs, GPUs, NPUs, and automotive SoCs. * The role further involves benchmarking state-of-the-art open-source models, collaborating with software platform teams to improve deployment efficiency, and contributing to research publications, patents, and technical innovation initiatives. Basic Qualifications: * Candidates should hold a master or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Engineering, or a closely related field. * A strong understanding of transformer architectures, large language models, and modern deep learning techniques is required. * Experience with Py