Senior Autonomy Engineer (AI) – NODA AI

Location: Austin, TX (Hybrid on-site, with up to 20% travel)
Clearance Requirement: U.S. Citizen with the ability to obtain a security clearance

 

About NODA

NODA is a veteran-owned, venture-backed technology company that is transforming how unmanned systems collaborate in complex, mission-critical environments. We are developing next-generation solutions that enable the autonomous orchestration of heterogeneous unmanned systems across air, sea, land, and space with vital applications in the defense, intelligence, and commercial sectors.

We are building the autonomy layers that allow systems from multiple domains to operate in synchronized, adaptive, and resilient ways. Our autonomy engineers bridge the gap between high-level AI-driven mission intent and real-world execution across UAVs, USVs, and UUVs operating in contested and dynamic environments.

Joining NODA means working on meaningful technology that pushes the boundaries of autonomy alongside a team that thrives on innovation, rapid iteration, and collaboration.

The Role

We are seeking a Senior Autonomy Engineer to design and implement the autonomy stack that powers adaptive multi-vehicle orchestration. This role focuses on mission planning, path generation, behavior execution, and task allocation across heterogeneous fleets.

You will work at the intersection of AI reasoning frameworks, event-driven autonomy stacks (e.g., Zenoh, MQTT), and distributed multi-agent systems, ensuring mission intent can be reliably executed and adapted in real-world conditions. Success in this role requires strong technical foundations in robotics, practical systems thinking, and a passion for delivering robust autonomy where reliability is non-negotiable.

Key Responsibilities

• Design and implement autonomy modules including path planning, task allocation, behavior trees, and reactive execution models

• Develop and maintain the autonomy framework that integrates with orchestration (AI-driven mission planning) and world modeling

• Ensure AI-generated plans are executable, explainable, and safe across multi-domain robotic platforms

• Implement and validate navigation, control, and autonomy behaviors for UAVs, USVs, and UUVs using event-driven middleware (Zenoh, MQTT, or similar) and simulation environments

• Profile and optimize autonomy performance for edge compute hardware (Jetson, Raspberry Pi-class devices)