AI Acceleration Algorithm Engineer

Huawei Hong Kong Research Center(HKRC) • Full Time • Tai Po District, HK • 5d ago

About the role

We are looking for an AI Acceleration Algorithm Engineer to research, design, and productionize algorithms that improve end-to-end AI compute, retrieval, storage, and inference efficiency. The role emphasizes independent algorithmic innovation and measurable system-level benefits across accuracy, throughput, latency, memory capacity, and cost.

What you will do

Design inference optimization algorithms for dynamic batching, request scheduling, parallelism, distributed sharding, load balancing, and elastic resource allocation.
Research model-efficiency techniques including post-training quantization, quantization-aware training, low-precision inference, structured sparsity, pruning, and distillation.
Develop high-performance retrieval, vector-search, data-placement, storage-caching, and I/O optimization algorithms for AI workloads.
Build cost models, search strategies, and adaptive optimization algorithms that balance accuracy, throughput, latency, memory capacity, and operating cost.
Productionize algorithms in training or inference systems and evaluate them using reproducible baselines, ablations, and end-to-end metrics.

What we are looking for

Master’s degree or Ph.D. in Computer Science, AI, High-Performance Computing, Operations Research, or a related field.
Representative work in one primary area and meaningful experience in at least one adjacent area: retrieval, AI storage optimization, inference scheduling and acceleration, quantization, sparsification, or AI compute-platform optimization.
Evidence of an independent algorithmic contribution, rather than framework integration or parameter tuning alone.
Proficiency in C++/Python, Linux, CUDA or equivalent accelerator development, and mainstream frameworks such as PyTorch, TensorRT-LLM, vLLM, SGLang, or Megatron-LM.
Quantified results using metrics such as accuracy, throughput, TTFT, TPOT, latency, memory footprint, scalability, or cost.

Preferred qualifications

Production deployment at significant model, traffic, data, or cluster scale.
Strong publications, patents, open-source contributions, or benchmark results in a relevant area.

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