Staff Machine Learning Engineer - Model Optimization & Quantization

Staff Software EngineerJoin 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'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 DoDesign, 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 modelsBuild tooling to analyze, profile, and debug model accuracy degradation caused by quantizationIntegrate AIMET workflows with popular ML frameworks — PyTorch and ONNXDevelop APIs and developer-facing tooling to make AIMET accessible and easy to use for external customers and design partnersIntegrate 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 hardwareQuantize and validate a broad range of model families — vision transformers, LLMs, diffusion models, speech, and multimodal architectures — for deployment via AI HubDevelop and maintain automated quantization pipelines and evaluation harnesses to scale model onboarding across AI Hub's growing model catalogMinimum Qualifications:Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. ORMaster's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. ORPhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.Preferred Qualifications:3+ years of industry experience in machine learning, deep learning, or AI infrastructureStrong proficiency in Python, with hands-on experience in PyTorch, ONNX and/or TensorFlowSolid understanding of neural network architectures — CNNs, Transformers, LLMs, diffusion models, multimodal modelsExperience with model quantization techniques — PTQ, QAT, weight-only quantization, mixed-precision, sub-4-bit methodsHands-on experience quantizing LLMs (GPT, LLaMA, Mistral, Falcon, or similar families) for inference optimizationFamiliarity with AIMET, GPTQ, AWQ, SmoothQuant, or similar quantization frameworks is a strong plusExperience working with ONNX, TFLite / LiteRT, or other model interchange formatsUnderstanding of hardware constraints: memory bandwidth, compute precision (INT4/INT8/FP16/BF16), and NPU/DSP executionExperience collaborating across teams or BUs to drive technical alignment and model deliveryProficiency with git and software development best practicesStrong written and verbal communication skills — ability to write clean APIs, documentation, and engage directly with external developersExperience with C++ for performance-critical components is a bonusFamiliarity with ARM processors and mobile SoC architecture (Snapdragon) is a plusExperience with automated evaluation pipelines and model benchmarking at scale is a plusLevel of ResponsibilityWorks independently with minimal supervisionProvides technical guidance and mentorship to other team membersDecision-making is significant and affects work beyond the immediate teamRequires strong communication skills to convey complex quantization concepts to varied audiences — from hardware engineers and BU partners to external researchers and application developersHas meaningful influence on the AIMET product roadmap, AI Hub model catalog, and cross-BU quantization strategyTasks are open-ended; planning, prioritization, and problem-solving are core to the rolePay range and Other Compensation & Benefits :$158,400.00 - $237,600.00The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link.