Simulation & Digital Twin Engineer

Responsibilities

• Own simulation software configuration management, source control, build automation, and release processes to ensure traceable, repeatable, and validated digital twin environments throughout the product development lifecycle.
• Build a high-fidelity digital twin (physics, drivetrain, sensors) of the vehicle and environment.
• Develop and maintain validated vehicle dynamics models representing steering, braking, drivetrain, and vehicle responses.
• Develop scalable scenario generation frameworks for autonomy verification and validation.
• Generate synthetic training and validation datasets supporting perception development.
• Guarantee simulation scenarios cover all key autonomy modes and edge cases within defined ODD.
• Define simulation coverage metrics to quantify validation completeness and corner-case exposure.
• Develop ground-truth and sensor-fidelity models for camera, LiDAR, radar, GNSS, and inertial sensors.
• Support virtual validation initiatives to reduce physical testing requirements.
• Integrate ROS2 autonomy stack for closed-loop simulation.

Requirements

• Education: Bachelor’s degree in Software, Electrical, or Mechatronic Engineer or other related field required.
• Years of Experience: 2+ years of relevant experience required.
• Software Knowledge: Python, C++ or similar programming languages required; ROS2 architecture, Linux development environments, version control systems (Git) required.

Additional Requirements

• Required Skills: Digital twin development workflows. Experience with simulation platforms and Python-based simulation environments. Understand physics simulation, vehicle dynamics, and sensor modelling. Sim-to-real validation approaches. Experience integrating robotics systems into simulation. Ability to design test scenarios and evaluate simulation outputs. Experience using Git, Azure DevOps or equivalent source-control systems. Familiarity with automated build systems, CI/CD pipelines, and test automation.
• Preferred Skills: Experience with autonomy simulation platforms such as CARLA, Gazebo, Isaac Sim, or Omniverse. Strong understanding of ROS2 architecture. Domain randomization in synthetic data generation for AI/ML. LiDAR, camera, GPS simulation models. Experience with SIL/HIL validation methodologies.

Travel

~10% travel required to collect ground-truth data, validate simulation fidelity against field results, and support correlation between virtual and real-world testing.