As an Autonomy and UAS Engineer, you will design, develop, and deploy machine learning models that power intelligent behaviors on unmanned aircraft systems. You will work with advanced autonomy frameworks, including platforms such as Shield AI’s Hivemind to build resilient navigation, perception, targeting, and collaborative autonomy capabilities for the Department of Defense.

Design and train machine learning models for perception, object detection, tracking, and classification.

Develop reinforcement learning and autonomy algorithms for navigation and mission execution.

Implement sensor fusion models combining EO/IR, LiDAR, GPS-denied navigation, and telemetry data.

Optimize AI models for deployment on edge compute platforms such as GPU, TPU, and embedded systems.

Develop and integrate autonomy behaviors within platforms such as Hivemind.

Implement mission planning logic and adaptive decision-making algorithms.

Enable collaborative autonomy between multiple UAS platforms.

Build simulation-based training pipelines for autonomy validation.

Deploy containerized AI models to airborne and ground edge nodes.

Optimize inference latency and resource utilization.

Conduct hardware-in-the-loop (HIL) and software-in-the-loop (SIL) testing.

Develop secure software pipelines aligned to DoD cybersecurity standards.

Integrate AI outputs into tactical networks and mission command systems.

Implement CI/CD pipelines for rapid model iteration and field updates.