Machine Learning Engineer – Model Optimization & Quantization

Overview

Join the Qualcomm AI Hub team and help developers integrate machine learning into their products and experiences:  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 is 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.

We are seeking a talented and motivated Staff Software Engineer with expertise in optimizing and deploying ML models — especially for edge devices.

• Design, develop, and maintain quantization algorithms and compression pipelines within the AIMET framework (PTQ, QAT, mixed-precision, Ada Scale, 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

• …Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of relevant experience…
• …Master’s degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of relevant experience…
• …PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of relevant experience…

• 3+ years of industry experience in machine learning, deep learning, or AI infrastructure
• Strong proficiency in Python, with hands‑on experience in PyTorch, ONNX and/or Tensor Flow
• Solid understanding of neural network architectures — CNNs, Transformers, LLMs, diffusion models, multimodal models
• Experience with model quantization techniques — PTQ, QAT, weight-only quantization, mixed-precision, sub-4-bit methods
• Hands‑on experience quantizing LLMs (GPT, LLaMA, Mistral, Falcon, or similar families) for inference optimization
• Familiarity with AIMET, GPTQ, AWQ, Smooth Quant, or similar frameworks is a strong plus
• Experience working with ONNX, TFLite/LiteRT, or other model interchange formats
• Understanding of hardware constraints: memory bandwidth, compute precision (INT4/INT8/FP16/BF16), and NPU/DSP execution
• Experience collaborating across teams or BUs to drive technical alignment and model delivery
• Proficiency with git and software development best practices
• Strong written and verbal communication skills — ability to write clean APIs, documentation, and engage directly with external developers
• Experience with C++ for performance-critical components is a bonus
• Familiarity with ARM processors and mobile SoC architecture (Snapdragon) is a plus
• Experience with automated evaluation pipelines and model benchmarking at scale is a plus

• Works independently with minimal supervision
• Provides technical guidance and mentorship to other team members
• Decision-making is significant and affects work beyond the immediate team
• Requires strong communication skills to convey complex quantization concepts to varied audiences — from hardware engineers and BU partners to…