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Sr. Cloud AI Infrastructure Engineer

Tencent · US-California-Palo Alto, United States

Information Technology Infrastructure & Support Remote Full-time Posted 4 days ago

About this role

Business Unit What the Role Entails 1.Architecture Research: Conduct in-depth research into the underlying hardware logic of various AI accelerators; evaluate the power-efficiency ratio and suitability of different heterogeneous architectures in the context of Large Language Model (LLM) inference and training. 2.Operator & Performance Optimization: Design and optimize high-performance operator libraries for large-scale cloud computing environments; resolve long-tail latency issues in hardware scheduling, memory management, and distributed communication. 3.Interconnect Architecture Definition: Define the interconnect architecture ; drive the virtualization, standardized access, and efficient pooling of heterogeneous computing resources in the cloud

4.Technology Trend Analysis: Monitor global trends in semiconductors and accelerators; perform feasibility studies and experimental validation for the implementation of emerging technologies within cloud infrastructure. Who We Look For 1.Education: Master’s or Ph.D. degree in Computer Engineering, Electronic Engineering, Microelectronics, or a related field

2.Core Expertise: Expertise in GPGPU architectures or other mainstream AI accelerator architectures. 3.Programming & Frameworks: Proficient in parallel computing frameworks; deep understanding of low-level operator development languages (e.g., CUDA, Triton). 4.Network & Distributed Systems: Solid understanding of large-scale distributed systems, cluster topologies (e.g., Fat-tree, Torus), and high-performance network protocols

5.Industry Insight: Familiar with the architectural evolution of global leading computing enterprises; ability to objectively analyze the technical pros/cons and engineering challenges of different architectural paths. 6.Experience: Experience in the application, optimization, or architectural design of ultra-large-scale accelerator clusters is preferred. 7.Framework Optimization: Experience in the low-level adaptation and performance tuning of

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