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Software Engineer - Specializing in Quantization

FuriosaAISeoul HQ
On-site Full-time

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Experience Level

Experience

Qualifications

ResponsibilitiesDevelopment of model compression toolsAcquisition and performance validation of various quantized modelsDevelopment of advanced compression algorithms based on findingsMinimum QualificationsExtensive experience in PyTorch developmentExperience in commercial software developmentMinimum of 3 years of practical experience in a related fieldPreferred QualificationsKnowledge and experience in DevOps and MLOpsExperience using LLM inference tools such as vLLM and TensorRT-LLMKnowledge and experience in deep learning quantizationExperience working in a company related to deep learning acceleration

About the job

About the Algorithm Team - Model Compression Division

It is widely recognized that LLM quantization can significantly enhance inference efficiency. However, implementing this in real-world applications presents ongoing challenges. The Model Compression Division is dedicated to developing user-friendly model compression tools that address these challenges and empower customers to maximize the efficiency of their NPU.

When model compression tools incorporate hardware-specific optimizations, they can achieve greater efficiency. To meet this demand, we have developed proprietary tools equipped with optimization features tailored for our NPU, enabling the provision of an essential software stack that maximizes NPU performance.

The FuriosaAI Model Compression tool is continuously evolving, with a focus on increasing automation, scalability, and reliability, leading to a growing demand for enhanced capabilities. As such, we are seeking talented software engineers with substantial software engineering experience who aspire to advance their careers as Model Compression Engineers.

About FuriosaAI

FuriosaAI is at the forefront of developing cutting-edge technologies that enhance model compression and inference efficiency. Our innovative solutions empower businesses to leverage their NPU capabilities to the fullest, ensuring optimal performance in real-world applications.

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