Company
Qualcomm India Private Limited

Job Area
Engineering Group, Engineering Group > Systems Engineering

General Summary
The Advanced Technology R&D; team is seeking a highly motivated Multimodal AI Research Engineer to design, develop, and commercialize next-generation AI solutions across audio, video, and language domains.

In this role, you will work at the intersection of research and productization, building state-of-the-art multimodal models and enabling their productive deployment on Snapdragon platforms powering mobile devices, XR systems, and IEOT.

You will collaborate closely with algorithm, systems, hardware, and software teams to bring cutting-edge innovations from concept and prototyping to scalable, real-world deployments.

Key Responsibilities
Design and develop multimodal AI models across audio, vision, and language
Build solutions using transformers, diffusion models, and multimodal fusion techniques
Optimize models for Snapdragon platforms (CPU, DSP, NPU)
Collaborate across hardware, systems, and software teams




Transition research into production-ready solutions and support commercialization

Preferred Qualifications
Experience in multimodal AI, generative AI, or vision-language systems
Solid understanding of Transformers, CNNs, diffusion models
Proficiency in PyTorch or TensorFlow
Experience in on-device AI optimization and deployment
Ability to work in cross-functional global teams

Education & Experience
Master’s or Ph.D. in Electronics, Computer Science, Electrical Engineering, or related field
2+ years of experience in CV, NLP, audio, or multimodal AI domains in academia/Industry

Keywords
Multimodal AI, Generative AI, Vision-Language Models, Audio AI, Edge AI, Vision Transformers

Minimum Qualifications
Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.

OR

Master's degree in Engineering, Information Systems, C

📌 Senior Engineer – Multimodal Ml R&d Hyderabad
🏢 Qualcomm
📍 Hyderabad