AI Training/Inference Acceleration Algorithm Engineer

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

Job summary

Huawei invites applications for an AI Training/Inference Acceleration Algorithm Engineer in Singapore. You will lead R&D of acceleration algorithms for Agentic AI and Multimodal domains, optimized for next-generation AI compute architectures to maximize efficiency.

You should have a strong background in LLM architectures and MoE models, with proven experience in large-scale training, fine-tuning, or inference deployment, plus hands-on AI acceleration tech and Python proficiency.

Qualifications

• Master's or PhD degree in Computer Science, AI, Electronic Engineering, or related field.
• Solid foundation in LLM architectures and MoE models with experience in large-scale training, fine-tuning, or inference.
• Hands-on experience in AI acceleration technologies and optimization techniques.

Responsibilities

• Lead R&D of AI training and inference acceleration algorithms for next-gen compute architectures.
• Implement acceleration algorithms within proprietary AI frameworks and libraries with iterative optimization.
• Identify trends and pre-plan algorithms to maintain competitive AI computing platforms.

Skills

Python programming

Distributed computing

Model optimization

AI acceleration concepts

High-performance kernels

Education

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

Tools

Zero-redundancy optimizer techniques

Distributed parallel strategies (TP/PP/SP/VP/DP)

Memory optimization

KV-cache compression

Model weight/activation quantization

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.