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GPU Performance & Simulation Infrastructure Lead

Drevol LLC

Irving, Texas • Today

Easy Apply

Contract

Depends on Experience

Job Details

Skills

GPU
HPC
AI Simulation

Summary

GPU Performance & Simulation Infrastructure Lead

Accelerate the future of robotics and physical AI by solving high-impact compute bottlenecks at scale. In this high-visibility technical lead role, you will pioneer automated, agentic code optimization and push NVIDIA Omniverse, Isaac Sim, and distributed multi-GPU clusters to their absolute performance limits.

Core Stack

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

Key Responsibilities & Duties

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.

Required Qualifications

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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91172358
Position Id: 9134392
Posted 10 hours ago

Fitment

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Nooruddin Ghani

Senior Technical Recruiter @ Drevol LLC

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Job Details

Skills

GPU
HPC
AI Simulation

Summary

GPU Performance & Simulation Infrastructure Lead

Core Stack

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

Key Responsibilities & Duties

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.

Required Qualifications

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.

Dice Id: 91172358
Position Id: 9134392
Posted 10 hours ago

Fitment