Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Develop and execute Cloud and AI sales enablement strategies for corporate, SMB, and partner teams. Build learning paths, technical skilling programs, seller communities, and field connection mechanisms. Identify capability gaps, analyze KPIs and customer feedback, measure business impact, and use insights to improve sales motions, revenue growth, consumption, adoption, and renewal. Collaborate across sales, engineering, marketing, partner, and technical success organizations to scale enablement programs.

Leads Azure capacity automation and backend tooling programs across Engineering, Finance, Operations, and capacity planning teams. Translates business needs into requirements, user stories, acceptance criteria, and workflow specifications; manages dependencies, risks, governance, and delivery. Improves processes through automation, data analysis, reporting, and AI-powered solutions such as Microsoft Copilot. Drives stakeholder alignment, change adoption, data quality, documentation, and operational excellence across capacity allocation and prioritization workflows.

Leads AI innovation and transformation across North America Consumer while owning Microsoft’s retail labor strategy and execution across major U.S. and Canadian partners. Responsibilities include developing AI pilots, driving adoption, managing outsourced labor agencies, optimizing deployment and coverage, setting KPIs, analyzing performance, managing budgets and contracts, and partnering across Sales, Marketing, Finance, Analytics, and retail teams to improve customer experiences and business impact.

Own Windows Consumer Retail strategy across North America, including category strategy, P&L, assortment, programs, investments, and performance management. Drive unit growth and market share against Mac and Chrome while coordinating Retail, OEM, Silicon, Sales, Marketing, Worldwide, and Finance stakeholders. Lead and develop a team of Senior Category Managers, manage hiring plans, and transform business operations through AI-enabled insights and execution.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Design and deliver distributed storage systems for AI and machine learning workloads at hyperscale. Lead architecture, development, testing, deployment, and innovations in scalability, performance, security, and efficiency. Collaborate with AI researchers and cloud infrastructure engineers, mentor engineers, and guide technical strategy for high-availability, durable, high-performance storage supporting training and inference.

Build and optimize inference systems and tools for generative AI models, collaborate with researchers to improve performance, profiling, debugging, and deploy large-scale distributed inference solutions.

Lead the vision, strategy, roadmap, and global execution of AI-powered learning and workforce skilling products. Manage and mentor product managers, collaborate with engineering, design, research, marketing, and customer teams, and drive products from customer insight through launch and scale. Build AI-native experiences, improve product adoption and metrics, foster inclusive team practices, and deliver measurable outcomes for millions of learners.

Design and build petabyte-scale multimodal data pipelines and infrastructure (text, audio, images, video). Own and maintain systems like Spark, Ray, and vector databases, partner with pre/post-training teams to improve data recipes through experimentation, and support large-scale dataset storage and processing for AI model training.

Design, curate, and evaluate large-scale text datasets for LLM pretraining. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; and collaborate with Safety, Ethics, and Governance to ensure dataset quality, privacy, and responsible AI alignment.