Staff Machine Learning Engineer – Model Optimization & Quantization

About the Role 

Join the Qualcomm AI Hub team and help developers integrate machine learning into their products and experiences: https://aihub.qualcomm.com/. 

In this role you will develop tools to help developers optimize and deploy machine learning models on edge and mobile hardware. AIMET is Qualcomm's open-source library for state-of-the-art model quantization, and compression techniques. You will develop and support cutting-edge model optimization workflows — pushing the boundary of what's possible on resource-constrained hardware. Applications range from quantizing large language models (LLMs) and generative AI models to compressing latency-critical vision, audio, and multimodal networks for deployment on Qualcomm Snapdragon and other edge SoCs. 

For this role we are seeking a talented and motivated Staff Software Engineer with expertise in the optimizing and deploying ML models – especially for edge devices.  

What You'll Do 

• Design, develop, and maintain quantization algorithms and compression pipelines within the AIMET framework (PTQ, QAT, mixed-precision, AdaScale etc.) 

• Implement advanced quantization techniques including weight-only quantization, activation quantization, KV-cache quantization, and sub-4-bit quantization for LLMs and generative AI models 

• Build tooling to analyze, profile, and debug model accuracy degradation caused by quantization 

• Integrate AIMET workflows with popular ML frameworks — PyTorch and ONNX 

• Develop APIs and developer-facing tooling to make AIMET accessible and easy to use for external customers and design partners 

• Integrate AIMET in AI Hub Workbench Quantize job to enable Quantization at large scale. 

• Own end-to-end quantization and optimization of models published on Qualcomm AI Hub, ensuring they meet accuracy, latency, and power targets on Qualcomm hardware 

• Quantize and validate a broad range of model families — vision transformers, LLMs, diffusion models, speech, and multimodal architectures — for deployment via AI Hub 

• Develop and maintain automated quantization pipelines and evaluation harnesses to scale model onboarding across AI Hub's growing model catalog