About the Role: We are looking for an experienced Digital Twin Developer to lead the design and implementation of a next-generation simulation and analytics platform for connected transportation systems. This role focuses on creating real-time, physics-accurate digital replicas of road environments, vehicles, pedestrians, and infrastructure using tools like NVIDIA Omniverse technology or similar enterprise digital twin platforms. You’ll work closely with ML engineers, data scientists, and simulation experts to bring to life traffic participant’s behaviour onto the platform.
Key Responsibilities: • Develop scalable, interactive digital twins for autonomous driving simulation, including vehicle behaviour, traffic dynamics, pedestrian modeling, and V2X scenarios. • Integrate LiDAR, camera, and RADAR sensor data into high-fidelity 3D environments using Omniverse or VISSIM or similar. • Collaborate with AI/ML teams to inject ground truth data and synthetic variations to improve prediction accuracy. • Develop 3D assets / city visualizations and integrate with live data feed with accurate positioning.
Required Skills: • ~5 years of experience in simulation, game engines, or digital twin development. • 3D modelling tools like Maya / Blender & USD composer / presenter. • Proficiency in Python, C++, and 3D simulation platforms (e.g., Omniverse / Nucleus, CARLA, Unity and/or VISSIM). • Experience with integrating LiDAR, RADAR, and camera simulations. • Understanding of basic ML systems. • Familiarity with event-driven architectures, real-time streaming (Kafka, Flink), and CEP engines. • Deploying / Hosting digital twin platforms in cloud. • Hands-on experience with 3D modelling formats like USD, FBX, or glTF.
Nice to have skills: • Strong mathematical and physics fundamentals for kinematics and motion modeling. • Experience with HD maps, city visualizer, localization systems, and coordinate transformations. • Any Previous work in factory modelling, smart city infrastructure, or V2X communications would be an added advantage.
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