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

Lead an engineering team responsible for reliability processes and platforms, including incident management, maintenance, on-call, issue management, and production readiness. Own the roadmap, technical direction, delivery, adoption, and continuous improvement of operational systems. Partner across infrastructure, product, and security teams; lead major incident response; hire and develop engineers; and apply automation, AI, and operational insights to improve service reliability and reduce manual work.

Design and develop high-speed mixed-signal interfaces and analog circuits for next-generation NVLink products. Responsibilities include schematic design, transistor-level architecture, layout guidance, verification, simulation, behavioral modeling, noise and stability analysis, circuit optimization, and post-silicon characterization and debugging.

Leads technical engagements with VMware and ecosystem partners to design, validate, and deploy NVIDIA GPU-accelerated virtualization solutions. Maps customer workloads to NVIDIA technologies, supports vGPU, AI, data science, and visualization deployments, troubleshoots complex issues, drives platform adoption, and influences product roadmaps through customer feedback and cross-functional collaboration.

Lead and grow a software engineering team responsible for CUDA Math Libraries platform expansion and readiness. Own integration, qualification, compliance, validation, performance testing, defect triage, and technical roadmaps across specialized platforms. Collaborate with library, DevOps, QA, compiler, CUDA, release, and platform teams. The role requires hands-on high-performance numerical software development using C++ and Python, parallel computing, performance optimization, and software engineering leadership.

Lead strategy and execution for NVIDIA Hardware EDA infrastructure programs, including cluster bring-up, decomposition, operations, modernization, and cloud migration. Coordinate engineering teams and cross-company partners, establish planning and reporting standards, manage dependencies, and use metrics, OKRs, and KPIs to measure outcomes. Drive operational excellence, automation, continuous improvement, communication, and alignment across technical and business stakeholders.

Design, develop, verify, and maintain embedded system software and kernel drivers for NVIDIA Tegra ARM-based SoCs. Responsibilities include pre-silicon modeling, silicon bring-up, core OS enablement, hardware and software collaboration, unit testing, documentation, and system-level debugging. The engineer will also influence hardware architecture through design specifications and support next-generation SoC platforms.

Develop and support NVIDIA CUDA Math Libraries across emerging and specialized platforms. Responsibilities include platform integration, functional qualification, defect triage, bug fixing, performance benchmarking, regression resolution, and building CI/CD qualification pipelines. The role requires high-performance numerical software development using C++ and Python, parallel computing, numerical methods, and software architecture, with preferred experience in CUDA, GPU computing, math libraries, cross-platform development, and embedded or safety-critical systems.

Develop Rust-based, GPU-accelerated data processing frameworks and agentic retrieval pipelines for multimodal LLM applications. Build systems for dataset indexing, querying, deduplication, filtering, classification, benchmarking, and optimization. Collaborate with AI researchers and cross-functional teams to develop production tools, prototypes, and roadmaps. Apply prompt engineering, fine-tuning, profiling, and micro-optimization while supporting containerized cloud infrastructure, continuous delivery, peer reviews, and Gen-AI productivity products.

Leads NVIDIA’s OEM Solutions Architecture organization, coordinating technical teams through new product introduction, product integration, end-to-end solution development, and large-scale customer deployments. The role manages technical engagement priorities, resolves partner issues, aligns engineering and sales stakeholders, influences OEM strategy, and supports recruiting and team growth. It requires extensive OEM, data center deployment, solutions architecture management, cross-functional leadership, project management, and NVIDIA technology expertise.

Build and operate software automation for large-scale NVIDIA GPU infrastructure. Responsibilities include bare-metal provisioning, hardware validation, firmware and software upgrades, cluster lifecycle management, BMC and Redfish tooling, failure diagnosis, observability, incident response, and automated repair. The role supports NVL72 systems, BlueField DPUs, Linux, Kubernetes, and cloud or on-premises environments while coordinating across hardware, networking, platform, data center, and partner teams.