AI​/ML Engineer

AI/ML Engineer – 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

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.

Our AI/ML Engineers are at the cutting edge of autonomous intelligence, designing and implementing AI agents that seamlessly bridge mission intent with real-world execution through advanced reasoning frameworks and multi-domain orchestration capabilities.

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

We are seeking an AI/ML Engineer to design and implement intelligent agents that drive adaptive mission planning and orchestration across multi-domain unmanned systems. This role focuses on LLM orchestration frameworks, agent reasoning systems, and the deployment of AI models to edge computing environments on autonomous vehicles.

You will build systems that translate high-level mission intent into actionable autonomous behaviors, dynamically adapt plans as environments change, and provide explainable reasoning to human operators. Your work will integrate directly with our ROS autonomy stack while ensuring reliable AI performance on resource-constrained edge hardware.

• Design and implement LLM orchestration frameworks for mission planning and task decomposition across heterogeneous vehicle fleets
• Develop agent reasoning systems that bridge high-level mission objectives with executable autonomy commands
• Optimize large and quantize language models and agent frameworks for deployment on edge computing hardware (Jetson, companion computers)
• Manage the full lifecycle of AI agents including model versioning, prompt engineering, tool integration, and memory management
• Implement human-in-the-loop workflows that provide transparent, explainable AI reasoning to operators
• Integrate AI reasoning outputs with autonomy middleware (e.g.,
  ROS 2) to enable seamless mission execution across heterogeneous
• Build evaluation, monitoring, and logging systems to track agent performance, reliability, and cost in operational environments
• Develop safe deployment and rollback practices for AI agents in mission-critical scenarios
• Collaborate with autonomy engineers to ensure AI-generated plans are executable and safe across multi-domain platforms
• Validate agent behaviors through simulation-in-loop testing before field deployment
• Design AI systems that maintain effectiveness in denied, degraded, and contested communication environments

• 3+ years of experience in production AI/ML applications with emphasis on LLM deployment and orchestration
• Proficiency in Python and modern AI/ML frameworks (PyTorch, Transformers, Lang Chain, or equivalent orchestration tools)
• Experience with model optimization, quantization, and deployment to edge computing environments
• Understanding of distributed systems and real-time AI inference requirements
• Familiarity with MLOps practices, including model versioning, monitoring, and lifecycle management
• Knowledge of prompt engineering, agent framework design, and multi-step reasoning systems
• Experience with constraint solving, planning algorithms, or symbolic reasoning approaches
• U.S. Citizenship with the ability to obtain a security clearance

• Experience with multi-agent coordination frameworks and distributed AI reasoning systems
• Background in robotics or autonomous systems integration (ROS 2, navigation stacks, sensor fusion)
• Familiarity with reinforcement learning for planning and decision-making applications. Understanding; understanding of secure coding practices and adversarial robustness in AI-driven systems.
• Experience deploying AI models to embedded hardware (Jetson, Raspberry Pi, or similar edge devices)
• Exposure to simulation-in-loop and hardware-in-loop testing environments
• Knowledge of autonomous vehicle domains (UAVs, USVs, UUVs) and associated protocols
• Background in structured data preparation and feature engineering for AI ingestion
• Contributions to open-source AI or robotics projects
• Experience contributing to mission assurance and safety cases, including field-readiness reviews
• Experience collaborating with security and compliance teams on logging, auditability, and data-handling requirements for fielded AI systems

• Systems thinker able to connect AI reasoning outputs with real-world autonomous execution
• Fast learner with adaptability to rapidly evolving AI/ML frameworks and methodologies
• Safety-focused mindset with commitment to explainable, reliable AI in…