Anduril Industries
Software Engineer, Robotics Tracking and Fusion
About this role
Anduril Industries seeks a Software Engineer to develop advanced target tracking and sensor fusion algorithms for military defense systems. You'll lead a small team in designing and deploying state-of-the-art tracking solutions, from prototyping through production optimization, working with real-time systems and multi-sensor data integration.
What you'll do
- Lead and influence a small team's direction in target tracking and state estimation
- Prototype and deploy advanced algorithms for tracking, sensor fusion, and state estimation
- Develop high-performance real-time software for tactical systems, simulations, and decision support tools
- Design robust filters, estimators, and probabilistic reasoning systems for noisy sensor data
- Validate technology performance through high-fidelity simulations and statistical analysis
- Customize algorithms for mission-critical use cases and integrate solutions into the software development lifecycle
What they're looking for
- C/C++ and Python programming
- Kalman filters and particle filters
- Multi-target tracking algorithms (JPDA, MHT, PHD filters)
- Bayesian filtering and sensor fusion
- Signal processing for radar, lidar, EO/IR sensors
- Machine learning for tracking and recognition
- Linear algebra, optimization, and stochastic processes
- Big data pipelines and NoSQL databases
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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 implementing Kalman filters or particle filters and how you've optimized them for real-time performance
- Walk us through a complex multi-sensor fusion problem you've solved—what was your approach and how did you validate the results?