About the job
AI/ML Engineer – NODA AI
Location: Austin, TX (Hybrid work model, with up to 20% travel required)
Clearance Requirement: U. S. Citizenship with eligibility to obtain a security clearance
About NODA
NODA is a veteran-owned, venture-backed technology firm dedicated to revolutionizing the collaboration of unmanned systems in intricate, mission-critical settings. We are at the forefront of developing cutting-edge solutions that facilitate the autonomous management of diverse unmanned systems across air, sea, land, and space, with essential applications in defense, intelligence, and commercial industries.
As an AI/ML Engineer, you will be engaged in pioneering work in autonomous intelligence, crafting and deploying AI agents that effectively align mission intent with real-world execution through sophisticated reasoning frameworks and multi-domain orchestration capabilities.
Joining NODA means becoming part of a team that values innovation, rapid iteration, and collaboration, working on impactful technology that advances the field of autonomy.
The Role
We are looking for a talented AI/ML Engineer to design and implement intelligent agents that facilitate adaptive mission planning and orchestration across multi-domain unmanned systems. This position will center on LLM orchestration frameworks, agent reasoning systems, and deploying AI models in edge computing environments on autonomous vehicles.
You will develop systems that convert high-level mission objectives into actionable autonomous behaviors, adapt plans dynamically as situations evolve, and provide clear reasoning to human operators. Your contributions will integrate seamlessly with our ROS autonomy stack while ensuring dependable AI performance on resource-limited edge hardware.
Key Responsibilities
Design and implement LLM orchestration frameworks for mission planning and task decomposition across varied vehicle fleets.
Create agent reasoning systems that connect high-level mission goals with executable autonomy commands.
Optimize and quantize large language models and agent frameworks for deployment on edge computing hardware (e.g., Jetson, companion computers).
Oversee the entire lifecycle of AI agents, including model versioning, prompt engineering, tool integration, and memory management.
Develop human-in-the-loop workflows that ensure transparent and explainable AI reasoning for operators.

