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

Builds secure AI and agentic cybersecurity tooling, transforming research prototypes into production-ready services. Responsibilities include developing backend services, APIs, CLI tools, automation, and integration patterns; defining MVPs for ambiguous security and identity challenges; collaborating across research, product, enterprise security, and platform teams; and applying offensive security and adversarial thinking to address AI-specific risks such as injection, tool misuse, unauthorized access, data exfiltration, and unsafe delegation.

Lead the technical direction, architecture, development, testing, and global deployment of NVIDIA’s AI agent compute and virtual desktop platforms. Build secure, isolated, cloud-hosted compute services across AWS and Azure, and drive desktop streaming performance for GPU and general-purpose workloads. Partner with engineering, IT, and security leaders to develop a multi-year platform roadmap supporting AI agents and end users.

Leads engineering enablement for large-scale AI/HPC clusters, managing a support team, training and documentation, request intake and prioritization, specialist debugging coordination, and user community development. Partners across architecture, software, firmware, networking, storage, datacenter operations, performance, and program teams. Uses service metrics and user feedback to improve workflows, self-service guidance, resource allocation, and support quality.

Build and operate large-scale GPU cloud infrastructure through automation, self-healing systems, observability platforms, and infrastructure-as-code pipelines. Develop production-grade tools in Python or Go, manage Kubernetes and Linux environments, integrate data center and CI/CD systems, respond to incidents, and improve reliability through automation, monitoring, postmortems, and root-cause analysis.

Architect and develop production software for datacenter power and performance management. Responsibilities include real-time control policies, hardware-software interfaces, validation automation, silicon bring-up, debugging across firmware, drivers, hardware, and platform layers, and power optimization across racks and data halls. The role requires collaboration with GPU, ASIC, firmware, validation, and datacenter systems teams throughout the product lifecycle.

Conduct novel research in AI security and cybersecurity, focusing on securing AI models, agents, systems, and infrastructure. Develop tools and processes to identify and remediate vulnerabilities, investigate adversarial machine learning, publish findings, and collaborate across NVIDIA. Present complex technical results to security, machine learning, and business audiences while contributing to secure software development and AI safety.

Design, develop, optimize, and debug C++ software for ultra-low-latency WebRTC streaming. Implement real-time transport, security, media, and congestion-control protocols; contribute to standards and protocol solutions; define streaming quality KPIs; and integrate software with platform, connectivity, automotive, and hardware systems.

Investigate, assess, and remediate vulnerabilities across NVIDIA data center platforms. Responsibilities include vulnerability triage, exploitability analysis, CVSS and CWE assessment, security fixes, regression testing, dependency updates, automation, and coordinated disclosure. The role also improves secure development lifecycle practices through threat modeling, security reviews, testing, and security automation while collaborating across distributed engineering teams.

Leads engineering teams developing platform software for GPU-accelerated applications using Kubernetes, container runtimes, Kata Containers, and Confidential Containers. Responsibilities include enabling GPUs as Kubernetes resources, integrating software across NVIDIA’s technology stack, managing priorities, and delivering secure, reliable distributed systems. The role requires deep systems software expertise, engineering management experience, and hands-on knowledge of containers, Linux, cloud computing, virtualization, and confidential computing technologies.

Lead development and optimization of NVIDIA’s Aerial platform for software-defined 5G/6G RAN systems. Develop baseband software in C, C++, Python, and CUDA; optimize CPU, GPU, and NIC performance; troubleshoot and integrate complex modules; conduct testing, demonstrations, benchmarking, and lab trials; and collaborate with engineering teams, customers, and telecommunications partners on system integration and technical solutions.