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NODA AI is a veteran-owned, venture-backed company developing next-generation autonomy solutions for unmanned systems (UAVs, USVs, UUVs) operating in defense, intelligence, and commercial sectors. The company focuses on autonomous orchestration of heterogeneous systems across air, sea, land, and space domains in contested and dynamic environments.
As Senior Autonomy Engineer, you will design and implement the autonomy stack powering adaptive multi-vehicle orchestration. This role bridges AI-driven mission intent with real-world execution, focusing on mission planning, path generation, behavior execution, and task allocation across heterogeneous fleets.
Key responsibilities include: designing autonomy modules (path planning, task allocation, behavior trees, reactive execution); developing and maintaining the autonomy framework integrating orchestration and world modeling; ensuring AI-generated plans are executable, explainable, and safe; implementing navigation and control behaviors for multi-domain platforms using event-driven middleware (Zenoh, MQTT); profiling and optimizing autonomy for edge compute hardware (Jetson, Raspberry Pi); conducting simulation-in-loop and hardware-in-loop testing; contributing to safety and compliance practices; and collaborating across AI, networking, and systems engineering teams.
Required qualifications: 6+ years developing autonomy or robotics systems; proficiency in C++ and Python for real-time development; hands-on experience with event-driven/pub-sub middleware (Zenoh, MQTT, DDS); background in motion planning, navigation, and control systems; experience with state estimation and sensor fusion; familiarity with behavior trees, task allocation, or symbolic planners; knowledge of simulation environments (Gazebo, Isaac Sim, AirSim); U.S. Citizenship with ability to obtain security clearance.
Preferred: Rust proficiency; multi-agent coordination and swarm robotics experience; autonomous vehicle domain exposure; familiarity with MAVLink, JAUS, or STANAG 4586 protocols; AI planning, reinforcement learning, or constraint solver background; embedded/real-time optimization experience; defense or aerospace autonomy exposure; open-source robotics contributions.
The role requires systems thinking, strong problem-solving in simulation and field environments, cross-disciplinary collaboration, comfort with ambiguity in mission-driven spaces, and detail-oriented safety focus. Hybrid on-site in Austin with up to 20% travel.
