Software Engineer

Machine Learning Technologies, LLC; Irving (TX); On-site; USD 180,000 - 260,000; Full time

Job summary

Machine Learning Technologies, LLC seeks an experienced Lead Software Engineer to helm Simulation Foundation Platforms, GPU Computing, and Agentic AI initiatives in Irving, TX.

You will optimize CUDA/C++/Python workloads, lead performance projects across Omniverse and Isaac Sim, and scale compute-heavy simulations on multi-GPU clusters, containers, and cloud environments.

Qualifications

• Bachelor's degree in a technical field and 7+ years of software engineering experience.
• Strong proficiency in C++ and Python with CUDA expertise and NVIDIA GPU architecture knowledge.
• Hands-on experience with multi-GPU workloads, HPC concepts, and GPU-accelerated simulations.

Responsibilities

• Analyze and optimize CPU/GPU/memory/I/O workloads across NVIDIA Nsight tools.
• Refactor simulation code in CUDA/C++/Python to improve hardware utilization.
• Develop agentic AI systems for automated runtime performance optimization.
• Scale simulations across multi-GPU and distributed computing environments.
• Manage containerized GPU workloads with Docker, Kubernetes, and Slurm.
• Work with GPU cloud platforms (CoreWeave, AWS, Azure) and simulation engines (Omniverse, Isaac Sim).
• Create automated regression tests, observability, and benchmarking pipelines.
• Collaborate with robotics, perception, and AI teams to boost performance and scalability.

Skills

C++

Python

CUDA programming

GPU architecture

Nsight tools

Multi-GPU

Agentic AI

Education

Bachelor's degree in CS/CE/EE

Tools

NVIDIA Nsight

Docker

Kubernetes

Slurm

CoreWeave

AWS

Azure

Omniverse

Isaac Sim

Job description

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.

The candidate will lead performance optimization initiatives across NVIDIA Omniverse, Isaac Sim, and distributed multi-GPU environments. This role involves developing agentic AI systems for automated code optimization, improving simulation performance, and scaling compute-intensive workloads across GPU infrastructure.

Key Responsibilities:

• 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.
• 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.

Technical Environment:

CUDA, C++, Python, Agentic AI, NVIDIA Omniverse, NVIDIA Isaac Sim, NVIDIA Nsight, GPU Computing, Docker, Kubernetes, Slurm, Linux, CoreWeave, AWS, and Azure.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.