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

Lead investor relations analysis and earnings preparation, translate investor sentiment and market dynamics into insights, partner with finance and capital teams, guide a junior analyst, and shape external communications and investor messaging for executive leadership.

Lead design and implementation of high-performance distributed storage systems and protocols (object, block, file) for AI infrastructure. Set technical direction, author designs, mentor engineers, integrate with hardware (NVMe, DPUs, GPU-direct), optimize performance, build benchmarking and observability tooling, and drive cross-team deployments and incident response in multi-petabyte data center environments.

Design, deploy, and operate Kubernetes clusters across AWS and bare-metal datacenters. Build automation for provisioning, upgrades, and scaling. Own reliability, performance, and security of production workloads. Implement observability, logging, and alerting. Partner on cloud-native services and CI/CD; lead incident response and post-mortems; set platform standards and mentor engineers.

Operate and scale bare-metal Kubernetes clusters to thousands of nodes, manage incidents and on-call rotation, support customers, collaborate with HPC/datacenter teams, build automation and tooling in Go/Python, create control-plane services/operators, automate cluster lifecycle, and define SLOs/SLIs for platform reliability.

Lead design and implementation of a highly available GPU/CPU host and instance lifecycle control plane. Bridge firmware, kernel, DPU, and semiconductor architecture with distributed systems. Provide technical leadership, mentor senior engineers, set engineering standards, collaborate with product and data-center teams, and translate customer requirements into scalable infrastructure.

Lead Public Cloud FP&A: own consumption-based revenue forecasting, OpEx and headcount planning, GTM finance partnership, segment and pricing analysis, internal allocation, and executive reporting to drive revenue, margin, and capital efficiency.

Build and maintain scalable Kubernetes control-plane services, operators, and automation for GPU-aware orchestration. Partner on networking (CNI, InfiniBand/RDMA/GPUDirect), observability, inference platform services, tooling/CLIs, and on-call production support for large-scale AI clusters.

Design, develop, and maintain high-performance distributed storage software and protocols (file, block, object). Integrate with hardware (NVMe, DPUs, GPU-direct), optimize performance and scalability, troubleshoot production data centers, collaborate with networking, compute, control plane and observability teams, and lead/mentor engineering teams.

Design and implement a vendor-agnostic storage control plane for high-performance AI clusters. Build Kubernetes CRD-based orchestration, reconciliation loops, multi-tenant isolation, capacity and placement engines, and observability for petabyte-scale storage across diverse vendors and protocols.

Draft, review, and negotiate high-value commercial agreements (customer, data center, partner/reseller, distributor, marketing). Lead enterprise deal negotiations, advise cross-functionally on contract structure and risk, use legal technology to streamline contracting, and improve templates and playbooks while providing practical business-focused legal guidance.