Qualcomm
Senior Director of Engineering - AI Platform
$282K–$422K
San Diego, California · On-site · Director · Full-time · Posted Oct 27, 2026
- Experience
- 10+ years with a bachelor’s degree, 9+ years with a master’s degree, or 8+ years with a PhD in relevant engineering or related work. Preferred experience durations vary by area and include 5+, 6+, 8+, and 10+ years.
- Education
- Bachelor’s, master’s, or PhD in Computer Science, Engineering, Information Systems, or related field.
About the job
Leads Qualcomm’s AI software strategy and global engineering organization for Snapdragon Compute, delivering on-device AI capabilities across Windows, Android, and Linux platforms.
What you'll do
- Set AI platform strategy and lead development, optimization, benchmarking, and deployment of AI models and software across Snapdragon CPUs, NPUs, and GPUs.
- Lead engineering partnerships and co-development with Microsoft, OEMs, ISVs, and other ecosystem partners.
- Build and manage a global engineering organization and oversee execution, schedules, quality, and delivery across Snapdragon Compute programs.
What they ask for
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field and 10+ years of relevant experience; OR a master’s degree and 9+ years; OR a PhD and 8+ years.
- Relevant experience must be in Hardware Engineering, Software Engineering, Systems Engineering, or related work.
Nice to have
- Master’s degree in Computer Science, Engineering, Information Systems, or related field.
- Experience with machine learning frameworks, operating-system and hardware integration, embedded systems and ML optimization, programming languages, statistics and probability, technical leadership, matrixed organizations, senior leadership interaction, or operating budgets/project financials.
Pay
$281,600.00 - $422,400.00 USD base salary
Tools
Windows, Android, Linux, WinML, Windows AI Foundry, ONNX Runtime, Tensor Flow, Pytorch
AI focus
Leads on-device AI strategy and engineering, including generative, multimodal, language, vision, and agentic AI; model enablement, optimization, quantization, deployment, runtime integration, and benchmarking.
Model-written summary · the original posting is authoritative