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The role involves performing data analysis for datacenter applications, using machine learning for GPU systems, and providing insights on trends to enhance workflows.

Lead architecture for cloud-networking and design system architectures for DPUs and NICs. Build end-to-end solutions from application to hardware, write architecture specifications, evaluate new technologies, develop POCs, and collaborate globally with SW/HW teams and customers.

Leads architecture for cloud networking, security, DPU, and NIC solutions. Designs end-to-end systems spanning applications and hardware, researches technologies, develops proof-of-concept prototypes, writes architecture specifications, and collaborates with global software, hardware, product, engineering, and customer teams. Requires strong C, Python, Linux, networking, cloud, system architecture, and software development experience, with DPDK, OVS, OVN, container, Kubernetes, and data center expertise preferred.

Develop GPU-accelerated, PyTorch-based machine learning solutions, including Graph Neural Networks, foundation models, and ensemble models. Build and optimize distributed training and inference workflows, support CUDA-X integrations, improve high-performance code, collaborate with customers and cross-functional teams, and provide technical leadership and mentorship.

Leads multifunctional initiatives defining safety architectures and requirements for autonomous vehicles. Develops safety concepts and analysis supporting continuous software delivery, improves safety metrics and analysis tools, evaluates safety data, and communicates readiness results. Collaborates with perception, AI, motion planning, and architecture experts to ensure DRIVE products meet rigorous safety standards.

Lead technical and strategic partnerships with quantum processing unit builders and federal research organizations. Develop joint roadmaps integrating QPUs with NVIDIA GPUs, networking, and quantum software including CUDA-Q, cuQuantum, CUDA-Q QEC, and NVQLink. Coordinate cross-functional engineering and product teams, create reference architectures, run technical workshops and training, present at conferences, and advise senior scientific leaders on QPU-GPU integration, real-time control, decoding latency, and fault-tolerant quantum computing.

Leads NVIDIA’s Infrastructure Security Engineering organization for EDA clusters. Builds and operates security controls across compute, storage, and networking in on-premises and multi-cloud environments while preserving performance for long-running workloads. Owns the technical roadmap, security technology lifecycle, automation, instrumentation, compliance evidence, and cross-functional execution. Develops secure-by-default infrastructure and leads teams responsible for scalable enforcement, observability, mitigation, and security outcomes.

Leads multiple concurrent quantum computing R&D programs, translating research roadmaps into measurable execution plans and scalable technologies. Partners across applied research, engineering, product teams, and external organizations to prioritize work, manage risks, solve technical challenges, and advance quantum technologies toward fault-tolerant computing. Requires strong technical understanding of quantum hardware and software, exceptional communication, and extensive experience managing complex cross-functional technology initiatives.

Design and implement LLM-first APIs and documentation for CUDA-X math libraries. Develop tools, metrics, and processes to improve AI-agent code generation and transform library APIs into LLM-friendly formats. Collaborate with stakeholders on LLM-enhanced releases while prototyping solutions for code-generation platforms. Requires expertise in LLMs, fine-tuning, reinforcement learning, RAG, MCP, agent tooling, composable API design, and technical documentation.

Operate and improve large-scale Kubernetes and GPU clusters across public and private clouds. Build automation, observability, capacity-management, and reliability systems; define SLOs and SLIs; support production launches; lead incident triage and root-cause analysis; conduct blameless postmortems; and participate in on-call support for AI workloads.