Anduril Industries
Software Engineer - Modeling & Simulation
About this role
Anduril Industries seeks a Modeling & Simulation Engineer to develop and validate sensor and vehicle models for autonomous systems. You'll create simulation tools that accurately represent real-world hardware performance and collaborate across teams to optimize autonomous platform configurations.
What you'll do
- Develop accurate models of sensors (LiDAR, cameras, IMUs) and vehicles for simulation environments
- Validate that simulated performance closely matches physical hardware in real-world conditions
- Build software tools for visualizing, analyzing, and optimizing sensor and vehicle configurations
- Conduct technical performance evaluations including range, accuracy, and other sensor metrics
- Collaborate with perception, autonomy, and hardware teams to inform design decisions
- Refine models and tools iteratively based on physical testing and deployment feedback
What they're looking for
- C++ and Python programming
- Sensor modeling (LiDAR, cameras, IMUs, GPS)
- Unreal Engine or similar simulation frameworks
- Data visualization and dashboarding
- Physics-based modeling and simulation
- Systems integration and validation
- Analytical data interpretation
- Optimization algorithms
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Anduril Industries
Anduril Industries builds autonomous defense systems including underwater vehicles, unmanned aircraft, and electronic warfare platforms for the Department of Defense. The company is hiring across mechanical engineering, mission operations, software development, technical leadership, and advanced manufacturing roles to support the design, deployment, and production of these mission-critical systems.
- Website
- anduril.com
Likely interview questions
- Describe your experience modeling sensors like LiDAR, cameras, or IMUs in simulation. What frameworks or tools have you used, and how did you validate your models against real hardware?
- Tell us about a time you built software tools for visualizing or analyzing simulation data. What was the technical stack, and how did the visualization impact decision-making?