Microsoft
Director - AI Capacity Business Development
$131K–$272K
Redmond, WA +1 · On-site · Director · Full-time · Posted Sep 21, 2026
- Location
- Redmond, WA
Mountain View, CA - Experience
- 10+ years of relevant experience; OR equivalent experience. Preferred: 8+ years leading relevant initiatives and 12+ years in specified technology markets.
- Education
- Bachelor's Degree in Business, Engineering, Computer Science, Economics, Finance, Sciences, or related field; OR equivalent experience.
About the job
Leads Microsoft’s external AI capacity strategy and partnerships, including sourcing infrastructure, evaluating investment options, and negotiating commercial agreements.
What you'll do
- Develop AI capacity sourcing, allocation, and ecosystem strategy, including recommendations for internal and external infrastructure.
- Lead strategic partnerships, build/buy/partner/invest assessments, and complex commercial negotiations with AI infrastructure providers.
- Advise executives and coordinate cross-functional teams on capacity deployment, partner governance, and strategic decisions.
What they ask for
- Bachelor's Degree in Business, Engineering, Computer Science, Economics, Finance, Sciences, or related field AND 10+ years of relevant experience in a listed discipline; OR equivalent experience.
Nice to have
- 8+ years leading strategic partnerships, commercial negotiations, ecosystem development, or corporate strategy initiatives in the technology sector.
- 12+ years in cloud infrastructure, AI, semiconductors, datacenter technologies, networking, or enterprise technology markets; direct ecosystem experience and expertise in GPU economics, AI workloads, and capacity planning are also preferred.
Pay
USD $130,900 - $251,900 per year (base salary); USD $165,600 - $272,300 per year (base salary)
Tools
GPUaaS, hyperscalers, neoclouds, AI infrastructure, semiconductors, datacenter technologies, networking, GPU
AI focus
Shapes AI infrastructure capacity strategy, including sourcing and allocating capacity for AI workloads, developing partnerships with infrastructure providers, and assessing AI training and inference workloads.
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