Software Engineer

• Analyze and optimize workloads across CPU, GPU, memory, I/O, storage, and networking using NVIDIA Nsight tools.
• Refactor and optimize simulation codebases using CUDA, C++, and Python to improve hardware utilization and execution performance.
• Design and implement agentic AI systems for automated code analysis and runtime performance optimization.
• Optimize and scale simulation workloads using NVIDIA Omniverse and Isaac Sim across multi-GPU and distributed computing environments.
• Manage and optimize containerized GPU workloads using Docker, Kubernetes, and Slurm.
• Work with GPU cloud infrastructure, including CoreWeave, AWS, and Azure.
• Develop automated regression testing, observability, and performance benchmarking solutions.
• Collaborate with robotics, perception, and AI engineering teams to improve simulation performance and scalability.

We are seeking an experienced Lead Software Engineer specializing in Simulation Foundation Platforms, GPU Computing, and Agentic AI. The ideal candidate will have strong expertise in CUDA, C++, Python, and NVIDIA GPU architecture, with hands-on experience optimizing high-performance simulation workloads., * Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline.

• Minimum 7+ years of relevant software engineering experience.
• Strong programming proficiency in C++ and Python.
• Deep expertise in CUDA programming, GPU performance optimization, and NVIDIA GPU architecture.
• Strong experience with Agentic AI and autonomous agents for automated code and runtime optimization.
• Hands-on experience with NVIDIA Omniverse, Isaac Sim, or equivalent simulation engines.
• Practical experience with NVIDIA Nsight tools, including Nsight Systems, Nsight Compute, and Nsight Graphics.
• Strong understanding of multi-GPU systems, parallel processing, and distributed computing.
• Experience with Linux, Docker, Kubernetes, and Slurm.
• Proven ability to benchmark, debug, and optimize compute-intensive workloads.
• 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.