UAS Software Engineer

Booz Allen Hamilton•Honolulu, HI

22h

About The Position

UAS Software Engineer The Opportunity: As a UAS Software Engineer specializing in AI autonomy, 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 Hivemind developed by Shield AI, to build resilient navigation, perception, targeting, and collaborative autonomy capabilities for Army and joint clients. You will operate at the cutting edge of edge AI, computer vision, reinforcement learning, and real-time embedded systems, helping the Army transition from human-in-the-loop control to AI-assisted and autonomous mission execution.

Requirements

5+ years of experience in software engineering, focusing on AI/ML systems
Experience with Python and C++
Experience with deep learning frameworks such as PyTorch, TensorFlow, and ONNX
Experience building and deploying AI models on edge hardware such as NVIDIA Jetson, GPUs, and embedded platforms
Experience with robotics middleware such as ROS or ROS2
Experience with computer vision or autonomous navigation systems
Ability to obtain a Secret clearance
Bachelor's degree

Nice To Haves

Secret clearance
Master’s degree
ML, AI, or Solution Architecture Certification

Responsibilities

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.
Support flight testing and operational evaluations.
Develop secure software pipelines aligned to DoD cybersecurity standards.
Support RMF and ATO compliance.
Integrate AI outputs into tactical networks and mission command systems.
Implement CI/CD pipelines for rapid model iteration and field updates.