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The Senior System Software Architect will design, validate, and create enterprise AI factory architectures, collaborating with various teams to optimize deployment strategies and systems performance across compute, storage, and networking infrastructures.

The Senior Product Manager is responsible for driving the roadmap and delivery of NVIDIA Cloud Functions, engaging with design partners, and collaborating with the open-source community.

The role involves developing an inference framework for large language models and optimizing performance on embedded platforms, collaborating across teams and contributing to CUDA kernel development.

Develop and invent parallel, graph-based RTL analysis and optimization methods. Analyze RTL impacts on latency, power, DFT, clocking, and power delivery. Explore ML (LLMs, GNNs, GANs, RL) and high-performance algorithms for logic synthesis, clustering, and implementation. Drive end-to-end tool development and collaborate with design teams to deploy optimizations.

Design, build, and maintain scalable deep learning infrastructure, high-bandwidth data pipelines, APIs, databases, and services. Collaborate with research and domain experts to productize, release, and maintain deep learning products while serving as an engineering generalist.

Develop and integrate AI agents and ML models to optimize VLSI/chip design flows. Design algorithms, build software tools and frameworks, analyze data to inform agent-driven methodologies, and collaborate with VLSI, ML, and software teams to improve productivity and quality.

Lead ASIC methodology and RTL implementation to improve performance, power, and area. Develop infrastructure and methodology, deliver verified RTL, and collaborate with architects, designers, verification, and VLSI teams to produce high-performance GPU and accelerator designs.

Lead and deliver compiler software programs across cross-functional teams using Agile and Waterfall. Drive process development, coordinate global partners, report status to executives, manage risks, and resolve technical/resource conflicts to ship compiler and GPU-related releases.

Design, build, deploy, and run large-scale infrastructure services on NVIDIA hardware. Define SLOs and observability, automate toil, participate in on-call incident response, and consult with peer teams on systems design and reliability.

Develop innovative features for GPU performance profiling tools while collaborating with hardware and software teams. Essential for supporting CUDA developers worldwide.