Senior Deep Learning Solution Architect

Responsibilities

• Contribute to the development of open-source inference frameworks such as SGLang and vLLM, including feature and operator development, performance optimization, and model support, in collaboration with the community.
• Develop and optimize KV cache offloading frameworks for LLM workloads, supporting multi-level cache offloading and reuse across CPU, SSD, and remote storage to improve inference efficiency. (Team project: FlexKV)
• Drive R&D on compute performance in distributed training, and explore methods and technologies for performance optimization.
• Study computational challenges in machine learning systems, identify common needs and bottlenecks, and build example code, acceleration libraries, or frameworks accordingly.
• What We Need to See
• Over 5 years working experience in the technology industry, with master’s degree or above in computer science, mathematics, electrical engineering, automation, or related fields.
• Strong interest in accelerated computing, parallel computing, and heterogeneous computing, with the motivation to explore these areas in depth.
• Solid programming skills, with a good understanding of data structures and computer systems fundamentals.
• Strong learning agility, adaptability, and the ability to analyze, define, and independently explore technical problems.
• Ways to Stand Out from the Crowd

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