AI Inference & Training Acceleration Architect

Beijing Foreign Enterprise Management Consultants Co.,Ltd.; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Huawei is seeking an AI Training/Inference Acceleration Algorithm Expert to lead R&D for accelerating AI training and inference across Agentic AI and multimodal domains. The role focuses on optimizing for next-gen compute architectures and integrating advancements into proprietary frameworks.

The candidate will drive end-to-end implementations, optimize memory and compute, and anticipate trends such as long-sequence modeling and sparsity to sustain a competitive edge.

Qualifications

• Masters or PhD in CS, AI, EE or related field.
• Strong background in LLMs and MoE models.
• Experience with large-scale model training and inference.
• Hands-on AI acceleration tech incl. zero-redundancy optimizers and distributed parallelism.
• Expertise in attention kernels, KV-cache compression, and quantization.
• Proficiency in Python and major DL frameworks; kernel optimization is a plus.
• Understanding of hardware-software co-design and memory bandwidth considerations.

Responsibilities

• Lead research and development of AI training and inference acceleration algorithms for Agentic AI and Multimodal domains.
• Drive end-to-end implementation within proprietary AI frameworks and acceleration libraries.
• Oversee technical foresight and pre-plan for trends like long-sequence modeling and sparsity.

Skills

AI acceleration

LLM architectures

MoE models

Python

Distributed training

Hardware optimization

Quantization

Performance tuning

Tensor frameworks

Education

Master’s or PhD in CS/AI/EE

Tools

CUDA

cuDNN

MKL

Job description

Huawei is seeking an AI Training/Inference Acceleration Algorithm Expert to lead R&D for accelerating AI training and inference across Agentic AI and multimodal domains. The role focuses on optimizing for next-gen compute architectures and integrating advancements into proprietary frameworks.

The candidate will drive end-to-end implementations, optimize memory and compute, and anticipate trends such as long-sequence modeling and sparsity to sustain a competitive edge.