NVIDIA is shaping the future of accelerated computing, physical AI, robotics, and industrial digitalization. Our Omniverse, OpenUSD, Isaac Sim, Isaac Lab, PhysX, Newton, and accelerated computing platforms help teams build, simulate, optimize, and operate digital twins of factories, robots, vehicles, infrastructure, and complex industrial systems. We are looking for a versatile Omniverse Engineer in South Korea who enjoys solving meaningful engineering challenges with customers. You will partner with local and global engineering teams to diagnose technical issues, build solutions, improve workflows, and turn real-world requirements into product improvements. Together, we will help customers adopt NVIDIA technologies for simulation-ready assets, industrial digital twins, robotics simulation, and physically accurate virtual environments. You will succeed in this role if you combine strong software engineering skills with practical experience in physical simulation, robotics, digital twins, or engineering workflows. Experience with CAE, CAD/CAM/PLM, industrial automation, or production engineering is valuable. We welcome candidates from varied technical backgrounds who enjoy learning, sharing knowledge, and collaborating across teams.

Partner with strategic customers and NVIDIA teams to understand, reproduce, debug, and resolve challenges across Omniverse, Isaac Sim, Isaac Lab, OpenUSD, and related simulation workflows. Translate customer needs into clear bugs, prototypes, examples, documentation, tests, and product improvements that global engineering teams can act on. Build and optimize industrial digital twin, robotics, SimReady, OpenUSD, CAD-conversion, asset-validation, rendering, physics, and performance workflows. Develop tools, scripts, extensions, and automation that help customers validate assets, profile workloads, reproduce issues, and integrate Omniverse into engineering pipelines. Collaborate with Omniverse, PhysX, Newton, Isaac, rendering, OpenUSD, and performance teams to improve platform capability, usability, reliability, and scalability. Share best practices with customers, solution architects, DevTech, OVEX, and partners, and capture reusable knowledge in runbooks, examples, training, and agent skills.

B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, Mechanical Engineering, Robotics, Applied Math, Physics, Aerospace, or a related field, or equivalent practical experience. 7+ years of relevant software or simulation engineering experience, with strong Python and C++ skills and a record of building or debugging complex systems. Hands-on experience in at least one of these areas: physical or robotics simulation, industrial digital twins, CAE, CAD pipelines, real-time 3D systems, large-scale visualization, or performance optimization. Working knowledge of 3D data and scene pipelines, ideally including OpenUSD, CAD conversion, instancing, geometry processing, materials, asset validation, or simulation-ready asset preparation. Familiarity with robotics or industrial automation workflows such as ROS 2, Isaac Sim, Isaac Lab, URDF, MJCF, sensors, controllers, HIL/SIL, or sim-to-real validation. Experience using AI coding agents to accelerate development, debugging, and customer problem solving, plus the ability to author reusable agent skills that distill domain expertise into workflows others can apply. Clear communication in Korean and English and the ability to collaborate with customers, field teams, and engineers across cultures and time zones.

Experience with NVIDIA Omniverse, Isaac Sim, Isaac Lab, OpenUSD, PhysX, Newton, Warp, CUDA, or RTX technologies. Knowledge of physical simulation concepts such as rigid bodies, articulations, contact, friction, constraints, deformables, FEM, numerical solvers, or multibody dynamics. Experience with CAE tools such as Ansys, Abaqus, Nastran, LS-DYNA, Simulink, Simscape, Modelica, MATLAB, Adams, or RecurDyn. Background in automotive, robotics, manufacturing, shipbuilding, aerospace, electronics, industrial automation, or digital factories. Experience validating simulation against physical systems, optimizing large scenes or GPU workloads, or applying reinforcement learning, synthetic data, sensor simulation, or sim-to-real methods.

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