Large Model Quantization Algorithm Engineer

XG TECH PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

XG Tech PTE. LTD. in Singapore seeks an experienced Large Model Quantization Algorithm Engineer to develop quantization and model compression for LLMs, VLMs, and video generation models, optimizing accuracy, memory, and on-device performance.

You will collaborate with algorithm, compiler, and hardware teams to deploy efficient AI inference into production, adapt models across NPU/CPU platforms, and build automation tools for quantization workflows.

Qualifications

• Bachelor’s degree or higher in a quantitative field (Engineering, CS, Math).
• Familiarity with LLM/VLM quantization and deployment optimization.
• Hands-on PyTorch QAT development is advantageous.
• Strong Python and C++ programming skills.

Responsibilities

• Develop and optimize quantization algorithms for LLMs, VLMs, and video generation models (PTQ, QAT, and related techniques).
• Design and evaluate quantization schemes balancing accuracy, performance, and memory.
• Perform calibration and error analysis for accuracy degradation and optimization.
• Optimize edge/on-device inference (Prefill/Decode acceleration, KV Cache, fusion, weight compression, memory).
• Adapt and deploy models across heterogeneous hardware (NPU/CPU); collaborate on model conversion and engine tuning.
• Develop quantization toolkits, workflows, accuracy evaluation, and debugging tools.
• Collaborate with model/architecture teams on quantization-friendly architectures and training strategies.
• Track and apply emerging quantization research to production.

Skills

LLM/VLM algorithms

QAT (PyTorch)

Python

C++

Edge deployment

Education

Bachelor’s degree in Electronics/CS/Quantitative field

Tools

PyTorch

Job description

About Company

Founded in 2022, XG Tech is driving the future of smart vehicles. Its mission is to empower the digital transformation of automobiles, moving from distributed computing to a centralized, cross-domain platform.

XG Tech focuses on the intelligent cockpit—the next frontier of differentiation—while seamlessly integrating advanced driving systems. By reimagining cars as mobile living spaces, XG Tech aligns with the evolving trend of vehicles becoming the “third living space.”

Role Summary

As a Large Model Quantization Algorithm Engineer, you will develop quantization and model compression algorithms for LLMs, VLMs, and video generation models. You will optimize model accuracy, memory efficiency, and inference performance across NPUs, GPUs, and CPUs, bridging the gap between model algorithms and on-device deployment. You will work closely with algorithm, compiler, and hardware teams to bring efficient AI inference technologies into production.

Key Responsibilities

• Develop and optimize quantization algorithms for LLMs, VLMs, and video generation models, covering PTQ, QAT, and related model compression techniques.
• Design and evaluate quantization schemes to balance model accuracy, inference performance, and memory efficiency.
• Perform quantization calibration and error analysis, identifying sources of accuracy degradation and driving optimization solutions.
• Optimize edge and on-device inference, including Prefill/Decode acceleration, KV Cache management, operator fusion, weight compression, and memory optimization.
• Adapt and deploy models across heterogeneous hardware, including NPU and CPU platforms, working closely with compiler teams on model conversion, engine compilation, and performance tuning.
• Develop quantization and model optimization toolkits, including automated quantization workflows, accuracy evaluation, and visualization/debugging tools.
• Collaborate with model and architecture teams to develop quantization-friendly model architectures, training strategies, and inference optimization techniques.
• Track and evaluate emerging research in model quantization, compression, sparsity, and efficient inference, and drive relevant techniques into production.

How will you stand out

• Bachelor’s degree or above in Electronic Engineering, Computer Science, Automation, Operations Research, Statistics, Mathematics, or a related quantitative field.
• Familiarity with LLM/VLM algorithms and deployment optimization techniques, including model quantization, sparsity/pruning, and inference acceleration frameworks.
• Prior hands‑on experience with PyTorch Quantization-Aware Training (QAT) development is advantageous.
• Strong proficiency in Python and C++.