AI/ML Engineer

About the role

You will architect intelligent agents that translate high-level mission intent into executable behaviors across air, sea, land, and space domains. You will own the design of LLM orchestration frameworks that enable adaptive mission planning and dynamic replanning in response to changing environments. You will build reasoning systems that connect strategic mission objectives with low-level autonomy commands in a way that is transparent and explainable to human operators. You will ensure AI models run reliably on resource-constrained edge hardware without sacrificing performance or safety. You will work closely with autonomy engineers to align AI outputs with real-world execution constraints and safety requirements. You will implement robust evaluation and monitoring pipelines that track agent performance, reliability, and operational cost in live conditions. You will champion safe deployment practices that allow for rapid rollback and continuous improvement of AI capabilities in denied and contested environments.

Key facts

What you'll do

• Design and implement LLM orchestration frameworks that decompose mission tasks and coordinate actions across heterogeneous vehicle fleets.
• Develop agent reasoning systems that bridge high-level mission objectives with executable autonomy commands for unmanned platforms.
• Optimize large language models and agent frameworks through quantization and other techniques for deployment on edge computing hardware such as Jetson and companion computers.
• Manage the full lifecycle of AI agents, including model versioning, prompt engineering, tool integration, and memory management strategies.
• Implement human-in-the-loop workflows that provide explainable AI reasoning to operators, ensuring clarity and trust in autonomous decisions.
• Integrate AI reasoning outputs with autonomy middleware such as ROS 2 to enable seamless mission execution across air, sea, land, and space domains.
• Build evaluation, monitoring, and logging systems that track agent performance, reliability, and cost during real-world operations.
• Develop safe deployment and rollback practices for AI agents in mission-critical scenarios where failures could compromise objectives or safety.
• Collaborate with autonomy engineers to verify that AI-generated plans are executable, safe, and compliant with operational constraints.
• Validate agent behaviors through simulation-in-loop testing before progressing to hardware-in-loop and field deployments.
• Design AI systems that maintain effectiveness in denied, degraded, and contested communication environments where connectivity may be intermittent or unreliable.
• Work with cross-functional teams to align machine learning workflows with security, compliance, and field readiness requirements.

Requirements

• Bring 3 or more years of production experience in AI and ML applications, with a strong focus on LLM deployment and orchestration in real systems.
• Demonstrate proficiency in Python and modern AI and ML frameworks such as PyTorch, Transformers, and LangChain or equivalent orchestration tools.
• Show experience with model optimization, quantization, and deployment techniques tailored to edge computing environments and resource-constrained hardware.
• Understand distributed systems principles and the requirements for real-time AI inference in time-sensitive operational contexts.
• Follow MLOps best practices, including model versioning, monitoring, and lifecycle management to ensure reliability and traceability.
• Apply knowledge of prompt engineering, agent framework design, and multi-step reasoning systems to solve complex autonomous tasks.
• Leverage experience with planning approaches or symbolic reasoning methods to integrate high-level decision making with reactive behaviors.
• Maintain U.S. citizenship and be eligible for a security clearance, as this role requires access to sensitive defense and intelligence information.

Nice to have

• Hands-on experience with multi-agent coordination frameworks and distributed AI reasoning systems that operate across diverse platforms.
• A background in robotics or autonomous systems integration, including familiarity with ROS2, navigation stacks, and sensor fusion pipelines.
• Exposure to reinforcement learning techniques applied to planning and decision-making in uncertain environments.
• Understanding of secure coding practices and strategies for adversarial robustness in AI-driven systems that operate in threat-rich contexts.
• Experience deploying AI models to embedded hardware platforms such as Jetson, Raspberry Pi, or similar edge devices used in field operations.
• Participation in simulation-in-loop and hardware-in-loop testing environments that validate autonomous behaviors before live deployment.
• Knowledge of autonomous vehicle domains, including UAVs, USVs, UUVs, and associated communication protocols and data formats.
• Experience with structured data preparation and feature engineering to ensure high-quality inputs for AI and machine learning models.
• Contributions to open-source AI or robotics projects that demonstrate a commitment to community-driven innovation and reusable software components.
• Experience supporting mission assurance and safety cases, including participation in field-readiness reviews and rigorous validation activities.
• Experience collaborating with security and compliance teams to address logging, auditability, and data-handling requirements for AI systems deployed in operational environments.

Practical notes

This position may involve travel and requires U.S. citizenship plus clearance eligibility. Applicants must be able to meet the stated requirements and demonstrate relevant experience in production AI and ML roles. The compensation band reflects levels aligned with the Austin location and may be adjusted based on relevant experience. Noda Ai is committed to building effective teams, and successful candidates will contribute to meaningful autonomy capabilities that operate across multiple domains and challenging operational conditions.