About the job
Qualcomm AI Research is looking for world-class researchers in artificial intelligence to join our team. Members of our global team form a multi-disciplinary core research group within Qualcomm which spans software, hardware, and systems, and we contribute technology deployed worldwide by partnering with our business teams across mobile, compute, automotive, cloud, and IOT. We also perform and publish state-of-the-art research on a wide range of topics in artificial intelligence, ranging from general theory to techniques that enable deployment on resource-constrained devices. We are dedicated to pushing the boundaries of AI across Generative AI, LLM and Multi-Modal Foundation Models, Reasoning, Reinforcement Learning, Agentic AI, and Autonomy, with a strong emphasis on efficient on-device intelligence.
Responsibilities
Lead efforts in transitioning research into production-ready solutions, enabling real-world applications and commercial impact.
Conduct cutting-edge research in efficient generative AI: LLMs and Multi-Modal Foundation Models, LLM Reasoning, Reinforcement Learning, Agentic AI and others.
Develop creative solutions with consideration of practical challenges on devices
Implementation and evaluation of possible solutions in both simulation and on-device environments
Contribute to Qualcomm’s strategic initiatives in efficient AI and embedded intelligence.
Publish in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP).
Qualifications
Minimum
Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. 6+ months of experience developing and/or optimizing machine learning models, systems, platforms, or methods.
Preferred
Experience in LLM efficiency research such as efficient attention, inference acceleration, or KV cache compression
Experience in on-device AI deployment on mobile or edge devices
Familiar with model optimization techniques such as quantization and distillation.