AI Inference & Compression Engineer

Beijing Foreign Enterprise Management Consultants Co.,Ltd.; Singapore; Presencial; SGD 120 000 - 180 000; Tempo integral

Resumo da oferta

Huawei is seeking an AI Inference & Compression Engineer in Singapore to drive LLM inference acceleration and advanced video compression techniques. You will work on KV cache optimization, model quantization, and memory bandwidth bottlenecks during autoregressive decoding, as well as develop AI-based media coding components and ensure smooth deployment within production frameworks.

The role covers evaluating visual quality and AI performance using metrics like PSNR, VMAF, perplexity, latency,

Qualificações

• Master’s or PhD in Computer Science, Electronic Engineering, Mathematics, or related fields (PhD preferred).
• Strong understanding of video coding fundamentals including prediction, transform coding, quantization, and entropy coding with hands-on experience in HEVC/AV1/H.266/VVC.
• Solid knowledge of Transformer architectures and attention mechanisms, and memory bandwidth constraints in AI inference.

Responsabilidades

• LLM Inference Acceleration: research and develop compression algorithms to accelerate LLM serving.
• Classical Codec Development: design and implement advanced video compression algorithms to improve RD performance.
• AI-Based Media Coding: develop AI-based loop filters, optical flow, and rate control for video coding.
• Model Deployment & Fusion: optimize deep learning models for efficient inference and seamless integration of compression into deployment frameworks (e.g., vLLM).
• Performance & Quality Evaluation: assess PSNR, VMAF, perplexity, latency, and throughput for video and LLM systems.

Conhecimentos

Python

C/C++

PyTorch

TensorFlow

Transformers

LLM inference

Model optimization

Formação académica

Master’s or PhD in CS/EE/Math

Ferramentas

CUDA

vLLM

HEVC

AV1

Descrição da oferta de emprego

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.