• Analyze brand-new DL networks (LLM etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next-gen inference products. • Develop prototypes of the fastest kernels on present and future NVIDIA GPUs. • Define hardware and software setups along with measurements to evaluate performance, power consumption, and accuracy in current and upcoming chips. • Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams.

• Capture business requirements, translate requirements into functional design, user stories, technical design, drive end to end integration testing, support data set up and issue remediation during UAT, manage development team activities, develop hypercare support model • Define and architect AI agents for Supply Chain use cases, using the right frameworks, multi-agent coordination, RAG, deployment, monitoring, and life cycle management. • Be hands on in quick proof of concepts development to demonstrate technical feasibility and implement enterprise grade Agentic Supply Chain solutions  • Partner with Enterprise IT engineering, product, and research teams while evaluating LLMs, agentic frameworks, and NVIDIA’s own NeMo technologies. • Ensure integration with enterprise IT and Operations data sources and Industry’s best Agentic platforms with strong content security focus. • Drive architectural decisions across deployment models (on-prem, cloud, hybrid, containerized) to deliver scalable, reliable, and efficient solutions. • Lead design reviews, develop technical documentation, and guide developers in principles of architecture and code development. • Champion observability, monitoring, versioning, and telemetry to ensure trustworthy and auditable AI agents. • Influence Supply Chain Operations adoption of the platform by partnering with stakeholders across IT, supply chain and serve as a reference adopter providing feedback to strengthen NVIDIA’s ecosystem.

• As a key MMPLEX Video Design team member, you will document, implement, and deliver fully verified, high-performance, low-area, and power-efficient designs to achieve the design targets and specifications. • Participate in video-related design and analyze architectural trade-offs based on features, performance requirements, and system limitations. • Craft micro-architecture, implement in HLS/RTL, and deliver a fully verified, synthesis/timing clean design. • Collaborate and coordinate with architects, other designers, pre- and post-silicon verification, SOCD, emulation, back-end, and bringup teams to accomplish your tasks.

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GPU System Architect team’s work scope covers whole GPU pipeline(graphics, compute pipeline, memory system) and multi GPU, CPU and CPU interconnection, which provides good opportunity to deeply learn the latest cross unit new features in the new GPU architectures. The team works as the safety net of the chip. We catch function bugs in the HW by randomly generating tests and running them in various pre-silicon full chip platforms and debugging the failures. This works provides a good full chip view of GPU and has a big space to innovate. What you’ll be doing: • Get familiar with various GPU workload’s composition • Learn about what’s the usual feature metrics for GPU workload • Design and implement inventive solution to efficiently extract features from GPU workload • Verify the solution using direct and random GPU workload • Design and implement inventive solution simplify GPU workload while keeping the required features • Design and implement inventive solution to generate GPU workload according to required features • Design and implement inventive solution to generate GPU workload which has the same feature with a given test and randomize other (required) features • Thoroughly verify the solution on GPU functional simulator/full chip RTL/emulation/silicon platform. • Provide detailed and organized documentation and report out for the project.

• Help design, develop, and improve scalable infrastructure to support the next generation of AI applications, including copilots and agentic tools. • Drive improvements in architecture, performance, and reliability, enabling teams to bring to bear LLMs and advanced agent frameworks at scale. • Stay informed of the latest advancements in AI infrastructure and contribute to continuous innovation.

• Develop and optimize the control stack, including locomotion, manipulation, and whole-body control algorithms; • Deploy and evaluate neural network models in physics simulation and on real humanoid hardware; • Design and maintain teleoperation software for controlling humanoid robots with low latency and high precision; • Implement tools and processes for regular robot maintenance, diagnostics, and troubleshooting to ensure system reliability; • Monitor teleoperators at the lab and develop quality assurance workflows to ensure high-quality data collection; • Collaborate with researchers on model training, data processing, and MLOps lifecycle.