Sensor Rendering Engineer (NVIDIA Omniverse)

Caterpillar Inc.•Irving, TX

1d•$112,710 - $183,140•Onsite

About The Position

The Sensor Rendering Engineer is responsible for developing, validating, and optimizing high-fidelity sensor simulation and rendering capabilities within the NVIDIA Omniverse ecosystem. This role focuses on creating physically accurate virtual sensing environments that support robotics, autonomy, digital twin, and machine intelligence applications. The engineer collaborates closely with autonomy, perception, simulation, and visualization teams to ensure synthetic sensor data accurately represents real-world operating conditions and enables robust algorithm development, testing, and validation. Key areas of ownership include sensor modeling, rendering pipeline support, synthetic data generation, environmental realism, performance optimization, and the integration of camera, LiDAR, radar, and other virtual sensors into large-scale simulation environments. The role requires expertise in real-time rendering technologies, RTX-based ray tracing, Omniverse Isaac Sim, USD workflows, and sensor fidelity validation against real-world data. The ideal candidate combines strong software engineering skills with deep knowledge of computer graphics, physically based rendering, sensor physics, and simulation technologies to deliver scalable, accurate, and production-ready virtual sensing solutions.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, Graphics Engineering, or a related technical discipline.
5+ years of experience in software development, simulation, computer graphics, rendering, robotics, or autonomous systems development.
Strong proficiency in Python and C++ for developing simulation, rendering, and automation applications.
Hands-on experience with NVIDIA Omniverse, Isaac Sim, USD (Universal Scene Description), and RTX-based rendering technologies.
Deep understanding of physically based rendering (PBR), ray tracing, path tracing, and real-time graphics pipelines.
Experience modeling and simulating perception sensors, including cameras, LiDAR, radar, depth sensors, and inertial measurement systems.
Knowledge of sensor physics, sensor calibration, noise modeling, and environmental effects impacting perception systems.
Experience developing synthetic data generation solutions for machine learning, computer vision, or autonomous systems applications.
Strong understanding of 3D geometry, optics, image formation, photogrammetry, and computer vision fundamentals.
Experience working with large-scale simulation environments and digital twin technologies.
Familiarity with GPU programming concepts, rendering optimization, and performance analysis techniques.
Experience integrating simulation platforms with robotics, autonomy, perception, or machine learning workflows.
Proficiency with source control systems, CI/CD pipelines, and modern software engineering practices.
Strong analytical, troubleshooting, and problem-solving skills.
Demonstrated ability to work effectively in cross-functional teams involving software, autonomy, controls, perception, and simulation engineers.
Excellent verbal and written communication skills, including the ability to communicate complex technical concepts to diverse audiences.

Nice To Haves

Master's or Ph.D. in Computer Graphics, Robotics, Computer Vision, Artificial Intelligence, or a related field.
Experience developing digital twins for industrial, construction, mining, agricultural, or autonomous vehicle applications.
Familiarity with ROS/ROS2, NVIDIA Cosmos, Isaac Lab, OpenUSD, and synthetic data frameworks.
Experience validating virtual sensor models against real-world sensor datasets and field measurements.
Knowledge of machine perception, sensor fusion, localization, mapping, and autonomous navigation systems.
Experience with cloud-based simulation infrastructure and scalable compute environments.
Demonstrated technical leadership and mentoring experience.
Experience supporting end-end virtual validation programs for autonomous or intelligent machines.

Responsibilities

Develop and maintain high-fidelity sensor rendering solutions for camera, LiDAR, radar, depth, and other perception systems within NVIDIA Omniverse and Isaac Sim.
Design, implement, and validate physically based sensor models that accurately represent real-world sensor behavior and environmental interactions.
Create synthetic data generation pipelines to support machine learning, computer vision, perception, and autonomous system development.
Optimize RTX-based rendering workflows to balance sensor accuracy, scalability, and simulation performance.
Develop tools and automation frameworks for sensor configuration, calibration, testing, and data collection.
Collaborate with autonomy, perception, controls, and digital twin teams to define sensor requirements and validation criteria.
Build and maintain USD-based simulation assets, environments, and workflows that support enterprise-scale digital twin initiatives.
Model environmental effects such as lighting, weather, dust, fog, material properties, occlusions, and surface reflectance to improve simulation realism.
Correlate simulated sensor outputs with real-world datasets and field measurements to assess fidelity and drive continuous model improvements.
Integrate sensor simulation capabilities into virtual machine validation and autonomy testing frameworks.
Partner with software, graphics, and infrastructure engineers to deploy scalable simulation solutions across workstations, cloud, and high-performance computing environments.
Investigate emerging rendering technologies, AI-driven simulation techniques, and Omniverse platform capabilities to enhance sensor realism and productivity.
Establish best practices, standards, and documentation for sensor rendering, synthetic data generation, and simulation validation.
Provide technical leadership and mentorship in rendering technologies, sensor physics, computer graphics, and Omniverse development.
Support cross-functional teams in troubleshooting sensor-related simulation issues and improving virtual testing effectiveness.