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The role involves designing GPU memory systems, developing performance models using AI, and optimizing architecture based on analyses of benchmarks and workloads, while collaborating with engineering teams.

Lead architectural initiatives for NVIDIA's global compute platform, operationalize AI infrastructure, and drive migrations while ensuring performance and automation at scale.

Seeking a Senior Engineer in Deep Learning and Computer Vision to develop machine learning models for autonomous vehicles, utilizing NVIDIA's GPUs and collaborating with researchers.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Design, build, and maintain full-stack web applications and microservices for VLSI design workflows. Collaborate with users to gather requirements, analyze performance bottlenecks, support large-scale data platforms with high QoS, and develop scalable backend and frontend solutions for chip design productivity.

Verify GPU micro-architecture using formal verification tools: define scope, write and execute testplans, develop assertions and environment assumptions, reason about arithmetic designs and C reference models, debug RTL, improve flows/scripts, and communicate coverage to partners.

Design and implement high-speed digital front-end and back-end for DSPs and silicon photonics IPs: define micro-architecture, calibration algorithms, translate to RTL/firmware, create synthesis constraints, drive timing closure, evaluate PPA trade-offs, and lead post-silicon bring-up, testing, characterization, and cross-functional production handoff.

Lead product strategy and roadmaps for post-training and RL workflows on NVIDIA GPUs/CPUs. Collaborate with customers and internal teams to build scalable distributed frameworks, optimize training/inference pipelines, and drive go-to-market efforts for OSS and developer-focused products.

Serve as a trusted technical advisor to NVIDIA Cloud Partners to design, integrate, and deploy large-scale GPU AI/HPC cloud infrastructure. Lead PoCs and production deployments of models, frameworks, and software stacks, run technical workshops and presentations, collaborate with sales, engineering, and product teams, and drive customer adoption and consumption.