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

Build developer tooling, demos, and integrations for Runpod; produce technical content and run scalable programs (content, events, community); attend and speak at developer/AI conferences; surface product feedback and ship solutions autonomously to improve developer onboarding and platform adoption.

Design and maintain automated testing frameworks for cloud-scale distributed systems. Build performance, load, stress, integration, API, end-to-end, chaos, security, and infrastructure tests. Integrate continuous testing into CI/CD pipelines, analyze observability data, validate Kubernetes and microservices environments, and collaborate with Engineering and SRE teams to improve reliability. The role also participates in on-call rotations and helps establish QA strategy and standards across GPU infrastructure, serverless endpoints, storage, billing, and event-driven systems.

Provide first-line technical and non-technical support through email, phone, chat, and video. Manage and resolve L1 tickets, troubleshoot system behavior and configurations, document solutions, explain technical issues clearly, and escalate complex problems to L2 or engineering teams. The role requires shell scripting, Python, SQL, REST APIs, GraphQL, and customer support experience, with possible weekend and allocated-shift availability.

Lead Runpod’s accounting organization and strategy, overseeing financial close, US GAAP reporting, technical accounting, audits, controls, tax, compliance, equity accounting, and accounting systems. Build and develop the team, establish SOX readiness, partner with executives and investors, and support financings and strategic transactions. Drive automation and AI across accounting workflows while ensuring the function is scalable, accurate, and audit-ready.

Lead complex, cross-functional technical programs for Runpod’s distributed AI cloud platform. Partner with engineering, SRE, product, data, supply, and data center teams to plan delivery, manage dependencies, identify technical risks, support architecture and rollout reviews, define success metrics, and ensure launch readiness. The role requires hands-on distributed systems and cloud infrastructure knowledge, strong communication, quantitative analysis, and experience driving programs from planning through launch.

Own finance analytics for Sales and GTM, including pipeline forecasting, revenue projections, pricing and deal modeling, sales commission administration, and marketing ROI analysis. Partner with Sales, Marketing, and leadership to align targets, improve reporting infrastructure, and guide budget allocation. This hands-on role builds scalable processes from the ground up and translates financial data into actionable business recommendations.

Lead Runpod’s security organization and own the security posture of its cloud platform, infrastructure, and corporate environments. Responsibilities include security strategy, cloud and application security, SecOps, GRC, compliance certifications, secure software development, threat modeling, vulnerability management, incident response, vendor risk, security automation, and infrastructure security. Build and mentor a distributed security team while partnering with Engineering, Infrastructure, Product, GTM, and Legal to protect customer AI workloads and support scalable growth.

Secure Runpod’s multitenant GPU cloud infrastructure across bare-metal and virtualized environments. Responsibilities include designing workload and network isolation, hardening Linux kernels and container platforms, testing hypervisor and hardware security, developing low-level controls in C, Go, or Rust, securing GPU and PCIe interactions, and responding to infrastructure security incidents with forensic capabilities for ephemeral containers.

Own Runpod’s video program as a senior hands-on producer and player-coach. Edit technical videos, demos, launches, social cutdowns, paid ads, customer reels, and event wraps. Establish intake, production workflows, templates, measurement, and delivery standards while making final craft decisions. Build programmatic video pipelines with Remotion or equivalent, create motion graphics in After Effects, manage freelance partners, and ensure products are presented clearly to developer audiences.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.