AI Algorithm Engineer (AI Training/Inference Acceleration)

We have partnered with a renowned global leader in information and communications technology (ICT) infrastructure and smart devices. They are providing full-stack, all-scenario AI solution for products and services carriers, enterprises, governments, and individual consumers worldwide.

Our client is seeking a leading AI Training/Inference Acceleration Algorithm Engineer to join their team in Singapore.

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.

Interested candidates, who wish to apply for the advertised position, please click on "Apply Now". We regret that only shortlisted candidates will be notified.

EA License No.: 01C4394

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