Qualcomm
Staff Product Manager - Data Center AI Software
$182K–$273K
San Diego, California · On-site · Staff · Full-time · Posted Sep 17, 2026
- Experience
- 5+ years of Product Management or related work experience required; preferred: 8+ years of product management, software engineering, technical marketing, or related experience, and 5+ years in AI/ML, cloud infrastructure, distributed systems, or data center technologies.
- Education
- Bachelor's degree in Engineering, Information Systems, Computer Science, Humanities, or related field. Preferred: Bachelor's degree in a technical field; Master's degree or PhD in Computer Science, AI/ML, Electrical Engineering, or related field.
About the job
Leads product strategy and requirements for Qualcomm’s data center AI software stack, engaging customers and ecosystem partners to support adoption and deployment.
What you'll do
- Develop product requirements and roadmaps from customer feedback, market analysis, and business objectives.
- Own strategy for AI software capabilities, including LLM deployment, cloud-native infrastructure, model workflows, benchmarking, and developer experience.
- Engage customers and partners, support evaluations and deployments, and assess competitive offerings and emerging technologies.
What they ask for
- Bachelor's degree in Engineering, Information Systems, Computer Science, Humanities, or related field.
- 5+ years of Product Management or related work experience.
Nice to have
- Experience in AI/ML, cloud infrastructure, distributed systems, or data center technologies; knowledge of AI frameworks and cloud-native technologies.
- Experience with AI accelerator hardware, LLM serving and optimization, benchmarking, hyperscale customers, or launching enterprise software products.
Pay
$181,800.00 - $272,600.00 USD base salary
Tools
Kubernetes, Docker, PyTorch, TensorFlow, vLLM, SGLang, Hugging Face, Ray
AI focus
Product strategy and requirements for AI software stacks, including LLM optimization and deployment, AI inference, model onboarding and validation, benchmarking, and cloud-native AI infrastructure.
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