Autonomy SME, Lead

Booz Allen Hamilton•Washington, DC

1d•$112,800 - $257,000•Remote

About The Position

As an Autonomy and UAS Engineer, you will design, develop, and deploy machine learning models that power intelligent behaviors on unmanned 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. You will operate at the cutting edge of edge AI, computer vision, reinforcement learning, and real-time embedded systems, helping the military transition from human-in-the-loop control to AI-assisted and autonomous mission execution. As a technical lead, you will guide autonomy architecture decisions, mentor engineering teams, and support the integration of autonomy capabilities into operational military systems.

Requirements

5+ years of experience in software engineering, including 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
Experience leading technical teams, architecture decisions, or autonomy-focused development efforts
Experience integrating autonomous systems into operational, test, or simulation environments
Ability to obtain a Secret clearance
Bachelor’s degree in a Computer Science, Robotics, Aerospace Engineering, or Electrical Engineering field

Nice To Haves

Experience working in military exercises and war games
Experience with autonomy frameworks such as Hivemind, PX4, ArduPilot, NVIDIA Isaac, or ROS2
Experience with collaborative autonomy, swarming, or multi-agent mission execution
Ability to support flight testing and operational evaluations
Top 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.
Lead the design and implementation of autonomy architectures for UAS and multi-agent systems.
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.
Provide technical leadership and mentorship to engineers developing autonomy capabilities.
Support flight testing, operational demonstrations, military exercises, and customer evaluations.
Collaborate with government stakeholders, operators, and engineering teams to define autonomy requirements and roadmaps.