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

Partner with business teams to convert manual reporting and spreadsheet-driven workflows into trusted self-serve data products and automated tooling. Define metrics, build or scope dashboards and automation, triage and prioritize tool requests, write engineering-ready specs, and deliver lightweight fixes or handoffs to engineering. Measure success by reduced manual work, trusted dashboards, and time saved.

Partner with Engineering and Product to own project-level budgets, headcount and OpEx planning, ROI and NPI financial modeling, fleet and capacity optimization, cash flow forecasting, and executive reporting to drive investment prioritization and cost efficiency.

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.

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.

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.

Lead and grow the Platform product group (Security, Observability, Billing) as a player-coach. Own platform product strategy and roadmap, hire and manage PMs, partner with platform engineering, deliver horizontal capabilities for Lambda's AI cloud (billing, identity, telemetry), make data-driven decisions, and ensure enterprise-grade trust and accurate usage-based billing.

Own and improve reliability, performance, and capacity of Lambda's production storage fleet. Build monitoring, alerting, CI/CD, and self-healing automation; troubleshoot storage incidents and low-level I/O/network issues; automate deployments and incident workflows; participate in on-call to reduce MTTR.