AI Inference & Compression Engineer

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 Inference & Compression Engineer.

Key Responsibilities

• LLM Inference Acceleration. Research and develop advanced compression algorithms to accelerate LLM serving. Focus on KV cache optimization, model quantization, and resolving memory bandwidth bottlenecks during autoregressive decoding.
• Classical Codec Development. Design and implement advanced video compression algorithms, focusing on improving Rate–Distortion (RD) performance, optimizing entropy coding, and enhancing quantization design for real-world applications.
• AI-Based Media Coding. Develop and optimize AI-based video coding components, including AI-based loop filters, optical flow, and intelligent rate control.
• Model Deployment & Fusion. Bridge the gap between AI research and production. Optimize deep learning models for efficient inference and ensure seamless integration of compression algorithms into deployment frameworks (e.g., vLLM).
• Performance & Quality Evaluation. Conduct rigorous objective and subjective visual quality assessments such as PSNR and VMAF for video systems, as well as perplexity, zero-shot benchmarks, latency, and throughput analysis for LLM systems.

Required Qualifications

• Master’s or PhD in Computer Science, Electronic Engineering, Mathematics, or related fields (PhD preferred).
• Solid understanding of video coding fundamentals including prediction, transform coding, quantization, and entropy coding with hands-on experience in standards such as H.265/HEVC, AV1, or H.266/VVC.
• Strong understanding of Transformer architectures and attention mechanisms, as well as key performance bottlenecks in generative AI inference, particularly memory bandwidth constraints (“memory wall”).
• Strong proficiency in Python and C/C++. Hands-on experience building, training, and modifying models using PyTorch, TensorFlow, etc.

Preferred Qualifications

• ISP Knowledge. Familiarity with Image Signal Processing flow, such as demosaicing, denoising, and tone mapping.
• Image Processing. Experience in computer vision-based image enhancement (e.g., de-blurring, artifact removal, or HDR).
• Hardware Optimization. Knowledge of SIMD, CUDA, or other hardware acceleration techniques for video and tensor processing.