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

Lead worldwide partner performance and growth by developing strategic partner business and go-to-market plans, driving co-sell execution, aligning C-suite priorities, resolving escalations, enabling partner capabilities, and accelerating cloud consumption and revenue through cross-functional collaboration.

Design and lead Azure infrastructure and application modernization migrations for enterprise and government customers. Architect secure, scalable, reliable solutions; lead proofs of concept and production deployments; troubleshoot technical blockers; and provide technical program management through a factory delivery model. Build customer and partner relationships, drive Azure consumption and adoption, mentor teams, develop reusable intellectual property, and provide feedback to influence Microsoft product improvements.

Leads customer R&D transformation engagements from pre-sales through delivery. Shapes scientific and technical challenges into AI architectures, product backlogs, MVPs, simulations, integrations, data pipelines, and production solutions. Partners with executives, researchers, engineers, product teams, and account teams to define outcomes, scope delivery, assess data and compute needs, and establish roadmaps. Develops reusable architectures and delivery playbooks while influencing Microsoft Discovery product direction and scaling AI-powered R&D solutions across industries.

Design and implement high-performance networking infrastructure for large-scale AI training on Azure. Lead architecture, produce secure, maintainable code, define test strategies, use AI tools for development and automation, review code, and collaborate across teams to ensure performance, scalability, reliability, and observability.

Own product strategy and multi-year roadmaps for GPU back-end networking. Translate AI workload needs into requirements, lead products through qualification and deployment, coordinate engineering and business teams, influence suppliers, and improve performance, reliability, cost, observability, and launch readiness.

Lead complex R&D and scientific AI engagements from pre-sales through production delivery. Partner with executives, researchers, engineers, and product teams to define use cases, architectures, backlogs, MVPs, simulations, integrations, data pipelines, and success metrics. Assess technical feasibility, coordinate multidisciplinary workstreams, and develop reusable architectures and delivery playbooks. Apply AI, machine learning, cloud computing, scientific workflows, and HPC to transform research processes while influencing product roadmaps and scaling solutions across industries.

Own forecasting, calibration, and stochastic optimization for AI economics and FinOps decisions. Develop probabilistic cost, demand, latency, quality, and delivery models; apply risk measures, causal inference, conformal prediction, Monte Carlo simulation, and scenario analysis. Deploy and monitor production-quality Python models using MLOps practices, partnering with engineering teams on telemetry and data lineage. Translate uncertain model outputs into customer recommendations and reusable analytical methods.

Lead the architecture, technical strategy, and roadmap for Microsoft's Windows ML compiler stack. Design high-performance compiler infrastructure, execution engines, optimization frameworks, and developer tools supporting CPU, GPU, and NPU workloads. Drive NPU enablement, graph optimization, scheduling, memory management, code generation, and hardware abstraction. Partner with silicon vendors and internal teams, influence AI hardware and platform strategy, lead cross-team initiatives, and mentor engineers developing on-device and generative AI capabilities.

Lead and run engagement systems for Microsoft Security field communities: design community strategy, communications, events, and feedback loops; collaborate with cross-functional teams to drive adoption of Security priorities; measure community health and impact; support planning, readiness, and change management for security field managers and individual contributors.

Design, implement, and operate cloud-scale secure data access features for Azure Blob Storage. Lead end-to-end development, collaborate with stakeholders, improve availability, observability, performance, and apply AI-assisted engineering to enhance productivity and operations.