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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - Model Optimization & Quantization - **Company:** Qualcomm - **Location:** Santa Clara, CA, United States (Remote available) - **Experience:** Experienced - **Salary:** $160,500.0 - $240,700.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, Artificial Neural Networks, C++ (Programming Language), Program Optimization, Information Systems, Computer Engineering, Software Debugging, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, AI Infrastructure, Software Organization, Pytorch, Large Language Models, Deep Learning, Git, Information Technology, ONNX (Open Neural Network Exchange) Format, GPT - **Published:** August 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e172d4da1c35e7c1 ## About the Role * 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., Master'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. OR PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work 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 TensorFlow * 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, SmoothQuant , or similar quantization 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 Level of Responsibility * 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 external researchers and application developers * Has meaningful influence on the AIMET product roadmap, AI Hub model catalog, and cross-BU quantization strategy * Tasks are open-ended; planning, prioritization, and problem-solving are core to the role ## Description * 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 ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Architecting the Future: Leveraging AI, Cloud, and Data for Business Success](https://www.wearedevelopers.com/videos/1096-architecting-the-future-leveraging-ai-cloud-and-data-for-business-success) - [Localized Open Models in Production: What Builders Need to Know](https://www.wearedevelopers.com/videos/100270-localized-open-models-in-production-what-builders-need-to-know) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Prompt Engineering is a Job of the Past](https://www.wearedevelopers.com/magazine/342-prompt-engineering-is-a-job-of-the-past) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [A 5-Step Open-Source Setup for Agentic Engineering](https://www.wearedevelopers.com/magazine/738-a-5-step-open-source-setup-for-agentic-engineering)