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Define technical vision and architecture for vehicle dynamics simulation. Implement production C++ interfaces, model-selection and parameter-identification workflows, translate control commands to time-aligned motion, validate and calibrate models using measured logs and test data, set standards and release gates, mentor engineers, and collaborate across planning, controls, simulation, and product teams.

Design and develop core components and performant frameworks for an autonomous driving platform, interfacing with vehicle sensors, optimizing parallel/GPGPU code (CUDA), performing in-vehicle tests and data collection, writing production C++ code, and ensuring quality and safety compliance.

Own and scale the end-to-end cloud execution pipeline for simulation assets, deploying GPU-aware containers and multi-GPU runtimes, implementing structural and runtime validation, AI-driven remediation, observability, CI/CD, and artifact pipelines to ensure reliable, automated cloud validation of simulation assets.

Work on performance and scalability of Kubernetes-based accelerated runtimes for AI, from orchestration to hardware. Design upstream Kubernetes changes, improve container startup and encrypted container performance, build large-scale simulation and CI-based tests, collaborate with researchers and upstream communities, and present results publicly.

Design, implement, and maintain Compute Sanitizer memory-checking tools for NVIDIA GPUs across Linux, Windows, Android and embedded RTOS. Collaborate with compiler, driver and architecture teams, write and review low-level, performant code, create tests, document designs, estimate work, and mentor junior engineers.

Design and scale production infrastructure for LLM-powered agents and copilots. Build reliable, high-performance services, APIs, CI/CD, and integrations; collaborate with research and hardware teams; mentor peers and drive continuous innovation in agentic AI applications.

The role involves managing NPI schedules, improving processes, leading cross-functional communications, and ensuring product readiness for production at NVIDIA.

Work on NVIDIA's OpenShell platform, developing distributed systems components, ensuring security, and managing operational tools for observability.

Design, demonstrate and optimize AI/ML and HPC solutions with hyperscalers. Build trust with customers, develop PoCs, run performance benchmarks on GPU and multi-node systems, present technical workshops, partner with sales/product teams, and troubleshoot system performance.

Lead integration and deployment of NVIDIA networking for hyperscale AI/HPC customers. Analyze, design, implement, and troubleshoot large-scale networks; develop PoCs; run technical meetings, debugging sessions, and workshops; partner with Sales and Product teams to define requirements and secure opportunities.