Top Tech Jobs & Startup Jobs in San Francisco, CA

Leads large-scale technical programs transforming Microsoft datacenter delivery. Drives cross-functional planning, execution, reporting, process improvements, and decision-making across engineering, construction, networking, finance, and operations teams. Uses technical expertise in datacenter systems and analytics to deliver projects on schedule, within budget, and with high quality. Manages ambiguity, executive stakeholders, risks, schedules, budgets, and organizational efficiencies across hyperscale cloud infrastructure initiatives.

Leads go-to-market strategy and execution for AI business solutions, including Copilot, agents, and AI consumption offerings. Develops localized growth plans, competitive positioning, product messaging, partner programs, sales enablement, pipeline acceleration, and performance metrics. Coordinates virtual, cross-functional teams across sales, marketing, finance, engineering, consulting, customer success, partners, and field organizations to drive customer acquisition, usage, revenue, and market share.

Lead deep-dive investigations into high-severity nation-state, supply chain, cloud, and identity incidents; perform root cause and blast-radius analysis, threat actor attribution, and hunting across Microsoft telemetry; drive cross-team containment and remediation, translate findings into detections and platform fixes, leverage AI/Copilot to accelerate analysis, and mentor peers to raise investigative quality.

Leads customer AI and cloud transformation engagements by translating business priorities into secure, scalable solutions. Designs and validates cloud architectures, supports migrations and adoption, identifies consumption and optimization opportunities, facilitates technical discussions, coordinates Microsoft, partner, and customer teams, and maintains CRM, project, usage, and forecast data. The role drives technical success, cloud consumption, and measurable customer business outcomes.

Lead and develop a team of Azure solution engineers to drive cloud migration, application modernization, AI adoption, and secure, governed platform solutions for Education, Nonprofit, and IGO customers across the Americas. Build trusted partnerships across sales, engineering, partners, and customers, scale repeatable solution plays, and coach technical talent to deliver measurable business and technology outcomes.

Design, implement, and operate highly scalable Azure Compute services and full-stack features. Collaborate on requirements, design docs, and dependency management. Drive reliability, performance, observability, and incident response as a designated responsible individual and on-call engineer.

Develop and execute go-to-market strategies for Microsoft's Frontier Transformation priorities across the Americas. Create customer engagement motions, field enablement resources, playbooks, and activation programs that connect business outcomes to technology solutions. Lead cross-functional collaboration across Sales, Services, Marketing, Customer Success, Product, and Industry teams. Analyze customer, sales, and usage signals, define success metrics, monitor program adoption, and provide executive-ready insights and recommendations.

Work with researchers and engineers to implement and optimize generative AI inference systems. Build tools for debugging performance, profiling, distributed systems, and improve compute efficiency across large heterogeneous GPU fleets. Deliver end-to-end solutions and processes to accelerate research and production inference.

Customer-facing Cloud Solution Architect who designs and deploys secure, scalable, AI-ready data platforms on Azure. Leads architecture sessions, proofs-of-concept, and production deployments, ensuring performance, resiliency, and security. Drives adoption, identifies expansion opportunities, enables partners, contributes to IP and technical readiness, and represents Microsoft in technical communities and events.

Lead end-to-end delivery of high-impact data science and AI projects: translate business needs, prepare data, build and deploy predictive, prescriptive, and generative AI solutions (including LLM fine-tuning and prompt engineering), ensure production readiness and responsible AI, and advise stakeholders to drive adoption.