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

Build and operate backend services, control planes, and automation for Lightning AI’s managed Kubernetes and Slurm infrastructure. Develop distributed systems for cluster provisioning, lifecycle management, scheduling, observability, reliability, and scalability across large-scale GPU environments. Diagnose complex production issues and collaborate with infrastructure, AI, and platform engineering teams. Contribute to architecture, technical design, mentoring, engineering practices, and on-call operations.

Own and scale Lightning AI’s global corporate tax function across a multi-entity organization. Lead federal, state, local, indirect, and property tax compliance; ASC 740 tax accounting; forecasting; audit support; M&A tax; risk management; and process automation. Build policies, controls, documentation, and a scalable tax operating roadmap while partnering with Finance, Accounting, FP&A, Legal, auditors, and external advisors.

Support multi-entity general ledger close activities, including journal entries, reconciliations, intercompany, payroll accruals, prepaids, fixed assets, leases, financial statements, and flux analysis. Help transform and document the close function through NetSuite implementation, process redesign, and control development. Support external audits and maintain accurate, auditable accounting records.

Designs, deploys, automates, and operates large-scale NVIDIA InfiniBand fabrics for GPU clusters. Manages UFM, switches, firmware, congestion control, routing, QoS, observability, troubleshooting, capacity planning, and incident response. Collaborates with AI platform, GPU infrastructure, storage, and systems teams to build reliable AI Factory environments.

Design and build backend systems for AI agent orchestration, tool execution, workflow management, memory, and state handling. Develop scalable APIs and reliable infrastructure for distributed AI workflows, improve platform reliability and observability, and partner with product, research, and engineering teams to deliver agent capabilities. Own technical initiatives from architecture through production, maintain software quality through testing and continuous delivery, and mentor engineers on backend and distributed-systems practices.

Develop and scale Lightning AI’s backend platform using Go, spanning APIs, infrastructure, billing, security, and integrations. Own features end to end, design reliable and scalable systems, improve architecture and performance, maintain software quality and continuous delivery, reduce technical debt, and mentor engineers. Collaborate with engineering, product, and design teams in a fast-changing SaaS environment.

Build and operate production software, APIs, tooling, and automation for large-scale GPU, bare-metal, and HPC infrastructure. Responsibilities include provisioning, configuration, monitoring, lifecycle management, observability, hardware integration, reliability improvements, and infrastructure capacity deployment. The role partners with networking, data center, platform, and infrastructure teams to design scalable systems and define technical direction.

Designs, implements, and maintains secure network infrastructure for Lightning AI’s production and AI environments. Responsibilities include firewall, VPN, IDS/IPS, segmentation, vulnerability assessment, penetration testing, SIEM monitoring, incident response, compliance documentation, access controls, and security automation. The role partners with infrastructure and engineering teams to mitigate threats and optimize security controls.

This is a general talent community opportunity rather than a specific open position. Lightning AI invites candidates to submit their information for consideration as future roles become available across U.S. and London hubs, with occasional remote opportunities. The company develops tools and infrastructure for building, training, and deploying AI systems and values ownership, urgency, communication, teamwork, continuous improvement, and long-term thinking.

Operate, scale, and optimize distributed storage infrastructure supporting large-scale AI/ML and HPC workloads. Build Python automation, manage Linux bare-metal systems, troubleshoot storage, hardware, networking, and operating system issues, and improve performance, reliability, monitoring, capacity planning, and lifecycle management. Collaborate with infrastructure, networking, platform, and data center teams on storage deployments and scaling strategies.