Edge Models Optimization Research Engineer
Pubblicato il 31-08-2026 - Huawei Italy Research Centers in Pisa
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. 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. 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. Benchmark state‑of‑the‑art open‑source models, collaborate with software platform teams to improve deployment efficiency, and contribute to research publications, patents, and technical innovation initiatives. 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 PyTorch and distributed training frameworks is expected, while previous research experience in model optimization, efficient AI, edge AI, or foundation models will be highly valued. Publications in top‑tier conferences or demonstrated open‑source contributions are considered a strong advantage. You will join a multidisciplinary research team working on some of the most ch