Qualcomm
Principal System Lead – AI Systems and Frameworks
$217K–$325K
San Diego, California · On-site · Principal · Full-time · Posted Sep 24, 2026
- Experience
- 8–10 years of relevant experience stated for the position; minimum qualification alternatives require 8+ years with a Bachelor's, 7+ with a Master's, or 6+ with a PhD. Also requires 8+ years in semiconductor product development and 5+ years in technical leadership.
- Education
- Bachelor's degree in Electrical Engineering, Computer Science, Computer Engineering, or related field; or Master's or PhD in a related field. Preferred Educational section says a Master's degree or Ph.D. in a related field is required.
About the job
Leads AI-focused mobile system architecture, translating OEM use cases into scalable, power-efficient implementations across Qualcomm hardware and software platforms.
What you'll do
- Map AI workloads to Qualcomm capabilities and propose system-level architecture enhancements across compute, memory, and power management.
- Analyze performance, power, memory, latency, model-size, and thermal trade-offs; identify system bottlenecks and recommend improvements.
- Collaborate across engineering and OEM-facing teams, communicate architectural recommendations, and contribute invention disclosures and patent filings.
What they ask for
- Bachelor's degree in a related field and 8+ years of relevant engineering experience; or Master's and 7+ years; or PhD and 6+ years.
- 8+ years of experience in one or more areas of semiconductor product development.
- 5+ years in a technical leadership role.
Nice to have
- At least 3 years making trade-offs between product KPIs and costs; at least 5 years in a large, matrixed organization.
- 3 or more years in a role involving direct interaction with senior leadership.
Pay
$216,600.00 - $325,000.00 (base salary)
Tools
Qualcomm mobile SoCs, Android AI frameworks, open-source ML runtimes, third-party SDKs, AI accelerators, CPUs, GPUs
AI focus
Mobile AI system architecture and optimization for generative AI, diffusion models, on-device inference, camera-centric AI, and always-on AI workloads.
Model-written summary · the original posting is authoritative