Large Model Quantization Algorithm Engineer

Kingfisher Recruitment Singapore Pte Ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Kingfisher Recruitment Singapore Pte Ltd is seeking a Large Model Quantization Algorithm Engineer to develop quantization and model compression techniques for LLMs, VLMs and edge AI deployments in Singapore. You will collaborate with algorithm, compiler and hardware teams to optimize accuracy, memory efficiency and inference performance across NPU, GPU and CPU platforms.

The role involves quantization schemes, calibration, on-device optimization and production-ready toolkits.

Qualifications

• Bachelor's degree or above in Electronic Engineering, Computer Science, Automation, Operations Research, Statistics, Mathematics or a related quantitative discipline.
• Familiarity with LLM/VLM algorithms and deployment optimisation techniques, including quantization, sparsity, pruning and inference acceleration frameworks.
• Strong Python and C++ programming skills.
• Hands-on experience developing quantization-aware training solutions with PyTorch would be an advantage.

Responsibilities

• Develop and optimise model quantization algorithms, including post-training quantization (PTQ), quantization-aware training (QAT), and related compression techniques.
• Design and evaluate quantization schemes that balance model accuracy, inference performance and memory efficiency.
• Perform quantization calibration and error analysis to identify and address sources of accuracy degradation.
• Optimise on-device inference through prefill/decode acceleration, KV cache management, operator fusion, weight compression and memory optimisation.
• Adapt and deploy models on heterogeneous hardware platforms, collaborating with compiler teams on model conversion, engine compilation and performance tuning.
• Develop model optimisation toolkits, including automated quantization workflows, accuracy evaluation and visualisation/debugging tools.
• Collaborate with model and architecture teams on quantization-friendly model architectures, training strategies and inference optimisation.
• Evaluate emerging research in quantization, compression, sparsity and efficient inference, and translate relevant techniques into production solutions.

Skills

Python

C++

PyTorch

Education

Bachelor's degree or above in Electronic Engineering, Computer Science, Automation, Operations Research, Statistics, Mathematics or a related quantitative discipline.

Job description

Large Model Quantization Algorithm Engineer | LLM / VLM / Edge AI

Our client is an innovative semiconductor company expanding its international R&D footprint, developing advanced computing platforms for intelligent vehicles and edge AI applications. As part of this expansion, the company is building its first overseas R&D team in Singapore and is seeking a Large Model Quantization Algorithm Engineer.

Role Overview

You will develop quantization and model compression algorithms for large language models (LLMs), vision-language models (VLMs), and video generation models. Working closely with algorithm, compiler and hardware teams, you will optimise model accuracy, memory efficiency and inference performance for deployment across NPU, GPU and CPU platforms.

Key Responsibilities

• Develop and optimise model quantization algorithms, including post-training quantization (PTQ), quantization-aware training (QAT), and related compression techniques.
• Design and evaluate quantization schemes that balance model accuracy, inference performance and memory efficiency.
• Perform quantization calibration and error analysis to identify and address sources of accuracy degradation.
• Optimise on-device inference through prefill/decode acceleration, KV cache management, operator fusion, weight compression and memory optimisation.
• Adapt and deploy models on heterogeneous hardware platforms, collaborating with compiler teams on model conversion, engine compilation and performance tuning.
• Develop model optimisation toolkits, including automated quantization workflows, accuracy evaluation and visualisation/debugging tools.
• Collaborate with model and architecture teams on quantization-friendly model architectures, training strategies and inference optimisation.
• Evaluate emerging research in quantization, compression, sparsity and efficient inference, and translate relevant techniques into production solutions.

Requirements

• Bachelor's degree or above in Electronic Engineering, Computer Science, Automation, Operations Research, Statistics, Mathematics or a related quantitative discipline.
• Familiarity with LLM/VLM algorithms and deployment optimisation techniques, including quantization, sparsity, pruning and inference acceleration frameworks.
• Strong Python and C++ programming skills.
• Hands-on experience developing quantization-aware training solutions with PyTorch would be an advantage.

We regret to inform that only shortlisted candidates would be notified.