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Work with Product, Engineering, Model/SW Devs, In-car testing, Fleet teams on test request and triage planning, do live triage with accurate analysis and debugging steps, present top issues by end of day.
Using NVIDIA Isaac Sim, Isaac Lab, and Matterix to build digital twins of robots, laboratory environments, and scientific procedures. Using NVIDIA Newton to simulate the physics of robots, articulated rigid bodies, deformable objects, granular media, and fluids.
Serve as NVIDIA's technical SME, engaging with internal and external stakeholders, customers, users and product teams to design developer tools, APIs, and workflows for chemistry and materials science.
Work on a unit level testbench, working on directed and random tests and test infrastructure, and contributing to the future direction of the methodology for the testbench.
Combine business insight and the data science toolkit to support critical finance data science initiatives using traditional machine learning, Bayesian statistics, simulation, and optimization.
Architecture Leadership: Define the long-term technical roadmap for communication libraries across NVIDIA’s next-generation platforms. You will ensure the seamless scaling of models to clusters comprising hundreds of thousands of nodes.
AI Factory deployment planning: Collaborate with NVIDIA product, engineering and partners teams to understand NVIDIA’s reference architecture for AI Factory infrastructure.
As a member of the GPU verification team, you will be responsible for verifying the ASIC design, architecture and micro-architecture of NVIDIA GPUs and SoCs using sophisticated verification methodologies.
Innovating and improving CPU performance through modern microarchitectural ideas. Modeling potential improvements to determine their merit and feasibility. Implementing effective solutions and collaborating closely with build, verification, and post-silicon teams.
Applying NVIDIA and OSS tools to define, build and orchestrate inference solutions. Enabling NVIDIA strategic Telecom ISVs to deploy and support the design defined above.
Research vulnerabilities on cutting-edge SoC and GPU designs. Develop and use cutting-edge security tools and techniques. Guide the continuous integration of latest mitigations into our security architecture.
Architecting an AI-native framework: Help design and build a codebase and architecture that scales beyond human capacity, supporting large numbers of AI agents working in parallel to generate, test, and validate production-grade software.
Our day-to-day work involves helping our partners be successful in their adoption of end-to-end AI solutions using NVIDIA's compute, networking, and software stacks.
Own validation coverage, technical risk, issue prioritization, and release-readiness recommendations from initial board bring-up through system integration and production release. Independently debug complex electrical, functional, performance, and reliability issues.
Actively collaborate with RTL, DV, FV, and Arch teams to then develop and implement design verification strategies for our GPU ASIC memory subsystem IP. Perform functional and performance verification using advanced tools and methodologies.
Architect and evolve GPU context-management mechanisms, including efficient methods for saving and restoring execution state.
Define consistent interconnect architecture, cache-coherency protocols, and high-performance on-chip interconnect interfaces. Contribute to interconnect IP block specifications, build, verification, and integration into the NoC fabric to ensure cache-coherency compliance.
Build complex agentic systems featuring multi-agent coordination, long-horizon reasoning, and advanced planning frameworks. Develop full-scale solutions, including domain-specific enterprise agents and high-performance retrieval pipelines (RAG) spanning various data sources.
Performing in-depth analysis and optimization to ensure the best possible performance on current and/or next-generation NVIDIA GPUs. Creating and optimizing core parallel algorithms, data structures, and reference codes to provide the best possible solutions for NVIDIA GPUs.
Developing innovative algorithms to accelerate simulation of QEC workloads at scale. Researching, developing and optimizing GPU-accelerated algorithms across multiple hardware generations.
Setting the long-term technical vision for distributed AI communication systems—GPU-to-GPU, GPU-to-storage, and cross-node data movement. Conducting original research and prototyping next-generation networking solutions over RDMA, NVLink, and GPUDirect.
Lead and develop a team of systems and networking engineers building distributed AI communication systems—libraries, frameworks, and system integrations.
Gather, define, and confirm business/process/data needs with stakeholders for IT and Ops system set-up (including SAP, SPNR, SERUS, etc.) and readiness for new products and site launches (e.g., LPU).
Drive development and productization of NVIDIA’s DGX datacenter products and L11 systems. Lead debug efforts for L11 rack-level integration, creating and applying tools/scripts for failure identification and root cause analysis.
US, USA