Qualcomm
DDR Validation Lead, Staff (AI Accelerator Platform)
$146K–$218K
San Diego, California +4 · On-site · Staff · Full-time · Posted Oct 16, 2026
- Location
- San Diego, California
Austin, Texas
Folsom, California
Hillsboro, Oregon
Santa Clara, California - Experience
- 5+ years in DDR/memory subsystem validation or silicon bring-up. The posting also lists alternate ASIC experience thresholds: bachelor's degree and 4+ years, master's degree and 3+ years, or PhD and 2+ years.
- Education
- BS, MS, or PhD in EE, CE, CS, or a related field.
About the job
Leads end-to-end validation of LPDDR5X and LPDDR6 memory subsystems for AI accelerator and flagship mobile platforms, from pre-silicon through post-silicon and production readiness.
What you'll do
- Define and execute DDR validation across emulation, FPGA, and silicon, including bring-up, training, performance, reliability, power, and stress testing.
- Lead debug of memory controller, PHY, firmware, SI/PI, timing, and system-level issues; validate interactions with processors, accelerators, caches, NoC, and software.
- Build automation, regression, and debug analytics, and characterize memory performance with AI workloads.
What they ask for
- BS, MS, or PhD in EE, CE, CS, or a related field.
- 5+ years in DDR/memory subsystem validation or silicon bring-up.
- Strong understanding of LPDDR architectures, DDR controllers and PHYs, memory training, cache/coherency/NoC, and low-power memory features; strong post-silicon debug experience.
- Proficiency in C/C++ and low-level software development.
Nice to have
- Mobile SoC, AI compute, automotive, or server platform validation experience.
- Familiarity with SI/PI analysis, memory performance tuning, and thermal characterization; first-silicon bring-up or volume production qualification experience.
Pay
$145,600.00 - $218,400.00 USD base salary
Tools
LPDDR5X, LPDDR6, LPDDR4/5/5X/6, C/C++, DDR controllers, PHYs, FPGA, NoC
AI focus
Validates DDR memory subsystems on AI accelerator platforms and characterizes bandwidth, latency, QoS, memory pressure, and AI workloads.
Model-written summary · the original posting is authoritative