R&D Engineer, XR AI Software and Systems
Qualcomm Incorporated (company site, opens in a new tab)3+ yrsBengaluruOn-siteDirect career pageEst. 18–35 LPA
Posted on 9 Sept
Reach people at Qualcomm Incorporated
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Qualcomm Incorporated you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop AI tools for tuning generative AI and multi-modal models targeting XR and wearables products.
- Research and develop techniques for AI inference optimization including graph optimization, quantization, pruning, and compression to balance performance, accuracy, latency, and power.
- Work on deployment and scaling of AI models (including GenAI, LLMs, and MVNs) for energy-efficient wearable devices.
Minimum Requirements
- Bachelor's degree (Engineering, IS, CS, or related) with 3+ years, OR Master's degree with 2+ years, OR PhD with 1+ year relevant Systems Engineering experience.
- Strong hands-on programming skills in Python and C++ (level >7/10).
- Experience with ML/AI tools development involving Deep Learning frameworks like PyTorch (1.x, 2.x), TensorFlow, Executorch, or ONNX.
- Strong understanding of model architectures in CV, NLP, LLMs, LVMs, multi-modal networks and inference graph optimization, quantization, and compression techniques.
Ideal Candidate Profile
- Experienced in performance tuning and troubleshooting of neural network accuracy and inference speed issues.
- Comfortable working on advanced optimization methods and researching industry trends for AI model acceleration on edge devices.
- Familiar with AI runtimes and frameworks, and has a systems engineering mindset towards integrating and scaling AI software solutions.