Focus Areas
Procedural scene generation in simulation (terrain, lighting, asset placement)
Synthetic data generation for training ML models across varied environmental conditions
Enhancement of physics-based sensor models for LiDAR and cameras
Automated test creation in simulation for construction site scenarios and machine behaviors (e.g. excavation with truck loading)
Required Qualifications
Master's level study (e.g. in robotics, computer science, mechanical or electrical engineering)
Advanced proficiency in Python
Working knowledge of C++
Experience with ML pipelines and 3D perception (e.g. object detection, point cloud processing)
Beneficial Skills
Experience with robotics simulation tools (e.g. Gazebo, MuJoCo, NVIDIA Isaac Sim)
Experience with ROS 2 or other robotics frameworks
Proficiency in Linux and Git
Familiarity with sensor modeling (LiDAR, cameras) or synthetic data generation
Experience conducting research (e.g. through an academic lab or previous internships)
Interest or prior experience in heavy construction or autonomous vehicles
Experience with software testing (unit, component, or functional tests)