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# Cloud AI Infrastructure Engineer

- **Company:** Tencent America
- **Location:** United States
- **Experience:** Expert
- **Salary:** $145,100.0 - $273,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Computing, Nvidia CUDA, Computer Engineering, Distributed Systems, Memory Management, General-Purpose Computing on Graphics Processing Units, Network Protocols, Performance Tuning, Software Architecture, Tensorflow, Pytorch, Large Language Models, Deep Learning, Parallel Computation
- **Published:** June 9, 2026
- **Apply:** https://www.dice.com/job-detail/29c72155-c50b-4c80-810e-066f230b763c

## About the Role

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 mainstream deep learning frameworks (e.g., PyTorch, TensorFlow) is preferred.

## Description

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.


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