Research & Development

Expert of Distributed Data Technology

In the distributed data system and AI data fields, take charge of key technological innovations related to large-scale distributed data systems and AI+big data, build core competitiveness of Huawei products in the industry, and help Huawei achieve business success through concept, architecture, and technological innovations.
Design and implement the full-memory data system architecture oriented to cloud-native serverless applications, build multi-semantic elastic scaling data service capabilities, and make breakthroughs and innovations in technologies such as near-consistent computing, distributed shared memory, and data access acceleration in heterogeneous hardware (GPU/NPU) scenarios.
Gain insight into and track the latest progress in the academia and industry, plan the evolution direction of cloud-native data system technologies, and build industry influence.

Computer or related major, master degree or above, more than 4 years of relevant work experience (3 to 5 years for PhD);
Deep understanding of distributed systems, cloud-native serverless architecture and theory, proficient in the design and implementation of distributed storage systems and cache systems, and rich engineering experience;
Familiar with GPU/NPU programming, and have rich experience in device memory management and access.
Have rich theoretical and engineering experience in new hardware optimization such as RDMA/NVMe/heterogeneous accelerator.
Leading or participating in related open source software (such as redis/kafka/RamCloud/Ray/flink) is preferred.
Have the key capabilities of mastering the distributed system from requirement analysis to architecture design.
Master key technologies of mainstream distributed frameworks and have in-depth understanding of emerging technologies in the industry.
Have a strong sense of responsibility, good communication and collaboration skills, and the ability to gain insight into industry development trends.
Successful work experience and professional accumulation required for the position to guide the project to success.

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