Qualcomm
SR, Product Manager
$165K–$248K
San Diego, California · On-site · Manager · Full-time · Posted Oct 5, 2026
- Experience
- 4+ years of Product Management or related work experience. Relevant Master’s degree substitutes for one year and Doctorate for two years. Preferred: 10+ years of product management experience.
- Education
- Bachelor’s degree in Engineering, Information Systems, Computer Science, or related technical field.
About the job
Leads the product strategy and full lifecycle for Qualcomm’s Windows on Snapdragon compute chipsets, defining AI platform products and specifications and enabling successful OEM device launches.
What you'll do
- Define PC chipset strategy, positioning, features, roadmap, and lifecycle plans using market, customer, technology, and ecosystem needs.
- Partner with engineering and cross-functional teams on product trade-offs, specifications, business cases, KPIs, milestones, and delivery.
- Engage OEMs and customers, support design-ins and device launches, and prepare technical presentations and product materials.
What they ask for
- Bachelor’s degree in Engineering, Information Systems, Computer Science, or a related technical field.
- 4+ years of Product Management or related work experience; completed relevant advanced degrees may substitute for up to two years (Master’s = one year, Doctorate = two years).
- Deep understanding of the PC processor market, including device categories, vendors, OEMs/ODMs, market requirements, and competitive dynamics.
- Knowledge of AI technology trends and workloads, AI software ecosystems, and relevant computing technologies.
Nice to have
- 10+ years of product management experience translating system solutions into shippable products and ratifiable specifications.
- Experience with AI accelerators across the app-to-silicon stack; MBA preferred.
Pay
$165,200.00 - $247,800.00 USD (base salary)
Tools
LLM architectures, PCIe, HBM, LPDDR, CPU, GPU, NPU
AI focus
Defines AI platform products and specifications spanning silicon, models, agentic systems, and reference devices; assesses AI workloads, on-device and hybrid AI, software stacks, and accelerators for computing products.
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