AI Training/Inference Acceleration Algorithm Engineer

Beijing Foreign Enterprise Management Consultants Co.,Ltd.; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

Huawei is seeking an AI Training/Inference Acceleration Algorithm Engineer to join in Singapore. You will lead R&D of acceleration algorithms for next‑gen AI compute architectures, aiming to maximize compute efficiency and utilization across agentic AI and multimodal domains.

Requirements include strong experience with LLM architectures, MoE models, distributed training, memory optimization, and high‑performance kernels.

Qualifications

• Master’s or PhD degree in Computer Science, Artificial Intelligence, Electronic Engineering, or a related technical discipline.
• Solid technical foundation in mainstream LLM architectures and MoE models, with proven experience in large-scale model training, fine-tuning, or inference deployment.
• Strong hands-on experience in AI acceleration technologies, including zero-redundancy optimizer techniques, distributed parallel strategies, communication compression, and memory optimization.

Responsibilities

• Lead the research and development of AI training and inference acceleration algorithms for Agentic AI and Multimodal domains, optimized for next-generation AI compute architectures to maximize compute efficiency and utilization.
• Drive end-to-end implementation of acceleration algorithms within proprietary AI frameworks and acceleration libraries, ensuring seamless integration and iterative optimization based on real-world performance metrics.
• Oversee technical insight and foresight within the training/inference domain, identifying trends such as long-sequence modeling and sparsity to sustain competitive advantage.

Skills

LLM architectures

MoE models

AI acceleration

distributed training

memory optimization

kernel optimization

Python

DL frameworks

hardware-software co-design

model tuning

100B+ models

Education

Master’s or PhD in Computer Science / AI / Electronic Engineering

Tools

TensorFlow

PyTorch

CUDA/CuDNN

Custom AI frameworks

Job description

On behalf of Huawei, a world-renowned information and communication technology company, we are seeking passionate and talented individuals to join our team as AI Training/Inference Acceleration Algorithm Engineer

Job Responsibilities:

• Lead the research and development of AI training and inference acceleration algorithms for Agentic AI and Multimodal domains, specifically optimized for next-generation AI compute architectures to maximize compute efficiency and utilization.
• Drive the end-to-end implementation of acceleration algorithms within proprietary AI frameworks and acceleration libraries, ensuring seamless integration and continuous iterative optimization based on real-world performance metrics.
• Oversee technical insight and foresight within the training/inference domain, identifying emerging trends such as long-sequence modeling and sparsity to pre-plan and develop cutting-edge algorithms that ensure the sustained competitive advantage of AI computing platforms.

Job Requirements:

• Master’s or PhD degree in Computer Science, Artificial Intelligence, Electronic Engineering, or a related technical discipline.
• Solid technical foundation in mainstream Large Language Model (LLM) architectures and Mixture of Experts (MoE) models, with proven experience in large-scale model training, fine-tuning, or inference deployment.
• Strong hands-on experience in AI acceleration technologies, including zero-redundancy optimizer techniques, distributed parallel strategies (TP/PP/SP/VP/DP), communication compression, and memory optimization.
• Technical expertise in high-performance attention kernels and KV-cache compression is essential, alongside proficiency in model weight/activation quantization and sparsity-aware acceleration.
• High proficiency in Python and deep familiarity with leading deep learning frameworks and high-performance acceleration libraries; expertise in hardware-level kernel optimization or native operator tuning is highly preferred.
• Deep understanding of hardware-software co-design, with knowledge of hardware characteristics (memory bandwidth, compute cycles, and interconnects) and a track record of optimizing performance for models with 100B+ parameters.
• Proven ability to perform complex AI model tuning and optimization in distributed or heterogeneous computing environments, translating hardware-specific features into significant algorithmic performance gains.