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This role involves leading product management efforts for ADAS in autonomous vehicles, focusing on product design, user experience, and team collaboration.

Develop and support RTL generation tools, automate workflows, enhance algorithms in C++ for logic insertion and Verilog processing, and improve tool quality.

Lead and manage the GPU Communications Libraries team at NVIDIA, overseeing project execution, quality, and library performance while collaborating with partners and stakeholders.

Design and implement an LLVM-based backend JIT compiler for NVIDIA GPUs. Develop compiler passes and optimizations, focusing on high performance and low compile time. Collaborate with hardware, compiler, and application teams to resolve issues and influence future GPU architectures.

Lead architecture, deployment, and operation of a hybrid HPC–quantum platform. Manage Linux-based GPU clusters, QPU hardware, schedulers (Slurm), networking, storage, and vendor integrations. Enable researchers by optimizing, benchmarking, and documenting hybrid workflows and supporting application performance and orchestration across classical and quantum systems.

Design, develop, test and maintain data-forwarding and offload features for Ethernet switches and SmartNICs. Implement routing, ACLs, QoS, NAT, VxLAN-EVPN and other network functions, collaborate with architects, QA and support, and lead multi-functional teams on large feature development and delivery.

The Senior Software Performance Engineer will optimize NVIDIA's autonomous driving solutions, enhance software architecture efficiency, and develop performance-driven product code in C++, collaborating with multiple teams.

Lead development and improvement of ASIC integration, build, simulation, CI, and debug infrastructure for high-speed IO GPU IP. Apply automation, metrics, and AI-assisted workflows to improve reliability, turnaround, and debuggability. Collaborate across design, verification, firmware, methodology, and infrastructure teams to root-cause failures and deliver durable process improvements.

Lead design, integration, and delivery of SONiC networking features for NVIDIA products. Develop high-quality C++/Python code in an open-source, continuous-deployment environment, collaborate with internal and external teams, manage chassis/networking features, and engage with the SONiC community to drive next-generation switch solutions.

Own the inference performance product roadmap across the full stack, turning advanced LLM optimization techniques into widely adoptable platforms. Define strategies for agentic and multi-turn workloads, partner with open-source and internal teams to land optimizations, and own benchmarking, release readiness, regression tracking, and production feedback loops to improve latency, efficiency, and cost per token.