Engineering Group, Engineering Group > Machine Learning Engineering
General Summary:
About us:
We are Qualcomm AI Research that are advancing AI to make its core capabilities – perception, reasoning, and action – ubiquitous across devices. Our mission is to make breakthroughs in fundamental AI research and scale them across industries. By bringing together some of the best minds in the field, we're pushing the boundaries of what's possible and shaping the future of AI. Welcome to visit our website at AI Research Areas | Intelligence on Devices | Qualcomm
Key Responsibilities:
- Design and develop end-to-end AI tools, models, and software to enable efficient deployment of quantized neural networks on cutting-edge Qualcomm hardware, with a strong focus on optimizing performance for speed, latency, memory, and power consumption
- Deploy AI models to Qualcomm CPU/NPU/GPU using the QNN SDK toolchain
- Develop debugging and profiling tools, as well as Qualcomm SDK solutions, to solve software issues and facilitate rapid on-device deployment of quantized models
- Build and maintain automated test suites leveraging modern testing tools and frameworks
- Collaborate closely with cross-functional teams to gather requirements and design development/test approaches
- Analyze test results and provide actionable feedback to the core development team
Preferred Skills and Experience:
- Proficiency in Python and C/C++
- Strong skills in software design, development, and debugging
- Solid understanding of LLM computation graph optimization, including graph fusion, memory optimization, and execution scheduling.
- Proven experience in optimizing LLM inference engines (e.g., vLLM, SGLang, llama.cpp) including scheduling and performance tuning for latency and throughput.
- Experience with operator/kernel-level optimization for LLM workloads, such as attention, matmul, KV cache access, and memory bandwidth optimization.
- Experience in Agentic AI pipeline and workflow
- Hands-on experience with generative AI models, including LLMs and LVMs
- Solid understanding of deep learning and experience with popular frameworks such as PyTorch, vLLM, SGLang with Hugging Face ecosystem
- Practical experience with Release + CI/CD Pipeline for end-to-end integration, regression, stress, performance
- Experience working with the Qualcomm QNN SDK is highly desirable
- Experience with Android programming and trace analysis is a plus
- Experience using AI coding assistants such as Claude code, Codex, or Cursor is a plus
Minimum Qualifications:
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field.
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