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# GPU Performance & Simulation Infrastructure Lead

- **Company:** NVIDIA Ltd.
- **Location:** Irving, TX, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, C++ (Programming Language), Cloud Computing, Computer Clusters, Static Program Analysis, Profiling, Nvidia CUDA, Computer Programming, Computer Engineering, Software Debugging, Linux, Distributed Systems, Python (Programming Language), Machine Learning, Performance Tuning, Regression Testing, Software Systems, Digital Twin, Graphics Processing Unit (GPU), Cloud Platform System, Parallel Computation, Agentic-AI, Isaac Sim, Containerization, Kubernetes, Information Technology, Slurm, Real-time Rendering, NVIDIA Omniverse, Code Restructuring, Docker, Data Generation
- **Published:** November 5, 2026
- **Apply:** https://www.dice.com/job-detail/35c24ad4-97b2-4b47-a92f-81913ef20374

## About the Role

* Languages: CUDA, C++, Python
 * AI & Simulation: Agentic AI, NVIDIA Omniverse, Isaac Sim
 * Tooling & Infrastructure: NVIDIA Nsight (Systems, Compute, Graphics), Docker, Kubernetes, Slurm
 * Cloud Environments: CoreWeave, AWS, Azure, * Education: Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
 * Core Languages: Strong programming proficiency in C++ and Python.
 * GPU Architecture & CUDA: Deep expertise in CUDA programming, GPU performance optimization, and NVIDIA GPU memory hierarchy/architecture.
 * Agentic AI: Strong background building agents for automated code analysis and runtime optimization.
 * Simulation Platforms: Hands-on experience with NVIDIA Omniverse, Isaac Sim, or equivalent simulation engines.
 * Profiling Tooling: Practical experience using NVIDIA Nsight tools (Nsight Systems, Nsight Compute, Nsight Graphics).
 * Distributed Computing: Solid knowledge of multi-GPU systems, parallel processing, and distributed computing environments.
 * Containers & Orchestration: Proven experience with Linux, containerized workloads (Docker), and cluster orchestrators (Kubernetes, Slurm).
 * Cloud Infrastructure: Hands-on experience deploying and optimizing workloads on GPU cloud platforms (CoreWeave, AWS, or Azure).
 * Benchmarking & Efficiency: Proven track record in benchmarking, debugging, and tuning compute-intensive workloads for maximum throughput and cost efficiency.

Preferred Skills & Experience

 * Optimizing RTX and real-time rendering workloads.
 * Background in robotics, autonomous systems, digital twins, or simulation platforms.
 * Knowledge of High-Performance Computing (HPC) environments and large-scale GPU clusters.
 * Experience with CI/CD pipelines and automated performance benchmarking frameworks.
 * Familiarity with machine learning infrastructure, AI training, and synthetic data generation.
 * Scaling simulation platforms used for autonomy, perception, or physical AI development.

## Description

* Workload Profiling & Diagnostics: Diagnose and eliminate performance bottlenecks across CPU, GPU, memory, I/O, storage, and networking layers using NVIDIA Nsight tools.
 * Code Refactoring & Tuning: Refactor and tune CUDA, C++, and Python simulation code to maximize hardware utilization, throughput, and execution speed.
 * Agentic Systems: Design and deploy agentic AI systems that autonomously analyze and optimize runtime code performance.
 * Simulation Scaling: Accelerate and scale simulation workloads on NVIDIA Omniverse and Isaac Sim across multi-GPU and distributed systems.
 * Cluster & Cloud Orchestration: Manage and optimize containerized GPU jobs using Docker, Kubernetes, and Slurm across CoreWeave, AWS, and Azure.
 * Observability & Benchmarking: Implement automated regression testing, observability, and performance benchmarking for continuous tracking.
 * Cross-Functional Leadership: Partner with robotics, perception, and AI teams to drive GPU performance best practices and evaluate next-generation hardware/software infrastructure.