Top Tech Jobs & Startup Jobs in San Francisco, CA

Designs, builds, and operates distributed storage and file-system platforms for large-scale AI training and inference on Azure. Responsibilities include optimizing Kubernetes and GPU workloads, developing caching architectures, improving reliability and observability, troubleshooting cross-layer production issues, and supporting geo-distributed storage and disaster recovery. The role leads architecture reviews, shapes storage strategy, mentors engineers, and collaborates across infrastructure, networking, operating systems, and AI platform teams.

Manages medium-enterprise customer accounts, builds C-level relationships, develops strategic account and renewal plans, drives cloud consumption, upsell, cross-sell, and pipeline growth, and forecasts revenue. Coordinates Microsoft teams and partners to deliver tailored technology solutions, improve customer satisfaction, and support digital transformation. Serves as the customer voice while maintaining Microsoft solution, industry, and licensing expertise.

Investigate attacker activity across cloud and on-prem environments, conduct threat hunts, analyze incidents, develop threat intelligence, and build prototype hunting tools. The role also recommends mitigations, notifies customers of threats, and feeds research findings into Microsoft product and tooling improvements.

Leads technical pre-sales engagements for strategic customers, translating business and AI requirements into secure, scalable Azure solutions. Conducts technical discovery, architecture design, demonstrations, proofs of concept, workshops, and production-readiness engagements. Influences technical decision makers, removes blockers, collaborates with partners and Microsoft teams, shapes customer win strategies, and accelerates Azure adoption, consumption, and production deployment.

Leads Microsoft Security’s AI SOC go-to-market strategy and operating model, translating Security Copilot and agentic SOC capabilities into repeatable customer adoption motions. Responsibilities include targeting, use-case selection, tenant enablement, activation, sustained usage, consumption, value realization, renewal, and expansion. The role coordinates business, technical, product, sales, field, delivery, and partner teams; analyzes customer and operational data; develops playbooks and feedback loops; and drives measurable security AI adoption without owning incident response or product engineering.

Develop and execute global technical strategies with Microsoft channel partners across AI, cloud, security, data, infrastructure, and modern work. Guide partners through cloud transformation, Centers of Excellence development, technical practice maturity, and scalable solution creation. Advise executives and architects, promote emerging technologies, build strategic relationships, communicate complex concepts, and collaborate with Microsoft engineering, field, and regional teams to drive partner adoption, consumption, and business outcomes.

Lead continuous improvement across AI transformation engagements by establishing kaizen-based operating rhythms, redesigning workflows, analyzing adoption and performance signals, refining prompts and agents, embedding standard work, and facilitating improvement cycles. The role drives measurable value, retires low-performing workflows and agents, and transfers continuous improvement capabilities to customer teams for sustained independent operation.

Drive technical sales and adoption of Microsoft AI and cloud platforms through PoCs, demos, hackathons, and architecture workshops. Design secure, scalable AI application architectures, resolve technical blockers, collaborate with engineering, and provide developer-facing thought leadership to accelerate production deployments and developer productivity.

Design and scale ultra-low-latency Ethernet and InfiniBand fabrics for hyperscale AI training. Engineer ROCE transport, congestion control (ECN/WRED/DCTCP), telemetry, routing, and observability; bring up clusters, benchmark AI workloads, debug performance, and collaborate with silicon and datacenter partners to deploy large-scale AI infrastructure.

Designs and integrates large-scale Azure network fabrics, including Ethernet, RDMA, and InfiniBand networks for demanding AI and cloud workloads. Responsibilities include troubleshooting physical network components, operating systems, APIs, and register sets; participating in on-call incident response; creating design and migration plans; leading network, code, testing, and security reviews; and developing reliable, scalable, secure automation and production solutions.