Anduril Industries
Software Engineer, Robotics Tracking and Fusion
About this role
Anduril Industries seeks a Software Engineer specialized in robotics tracking and sensor fusion to develop advanced algorithms for real-time target tracking and state estimation in defense applications. You'll lead a small team, prototype cutting-edge tracking systems, and integrate sensor fusion technologies into mission-critical platforms powered by Lattice OS.
What you'll do
- Define technical direction for tracking and state estimation initiatives within a small team
- Prototype and deploy algorithms for multi-sensor data fusion and real-time tracking systems
- Develop high-performance software for tactical implementations, simulations, and decision support tools
- Design robust filters, estimators, and probabilistic reasoning systems for noisy sensor data
- Validate system performance through high-fidelity simulations and statistical analysis
- Customize algorithms for mission-critical use cases and drive customer success
What they're looking for
- C/C++ and Python programming
- Kalman filters and particle filter implementation
- Multi-target tracking algorithms (JPDA, MHT, PHD filters)
- Bayesian filtering and sensor fusion
- Signal processing for radar, lidar, and EO/IR sensors
- Machine learning for tracking and classification
- Applied mathematics (linear algebra, optimization, probability)
- 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
- Can you walk us through a specific tracking algorithm you've implemented and how you optimized it for real-time performance?
- Describe your experience with multi-sensor fusion—how have you handled conflicting data from different sensor modalities?