AI/ML Engineer

NODA AI•Austin, TX

20d•Hybrid

About The Position

NODA AI is seeking an AI/ML Engineer to design and implement intelligent agents for adaptive mission planning and orchestration across multi-domain unmanned systems. The role focuses on LLM orchestration frameworks, agent reasoning systems, and deploying AI models to edge-computing environments on autonomous vehicles. The engineer will build systems that translate high-level mission intent into actionable autonomous behaviors, adapt plans as environments change, and provide explainable reasoning to human operators. The work integrates with NODA’s ROS autonomy stack and must deliver reliable AI performance on resource-constrained edge hardware. NODA is a veteran-owned, venture-backed technology company developing autonomous orchestration solutions for heterogeneous unmanned systems across air, sea, land, and space, with applications in defense, intelligence, and commercial sectors. The position is based in Austin, Texas, with a hybrid on-site arrangement and up to 20% travel. U.S. citizenship and the ability to obtain and maintain a DoD security clearance are required. Career progression may lead from AI/ML Engineer to Senior AI/ML Engineer, Staff AI/ML Engineer, and Principal Engineer; final leveling is determined at the offer stage.

Requirements

At least 3 years of experience in production AI/ML applications, with an emphasis on LLM deployment and orchestration.
Proficiency in Python and modern AI/ML frameworks such as PyTorch, Transformers, and LangChain, 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 is required due to ITAR regulations, along with the ability to obtain and maintain a DoD security clearance.

Nice To Haves

Experience with multi-agent coordination frameworks and distributed AI reasoning systems.
Background in robotics or autonomous systems integration, including ROS 2, navigation stacks, or sensor fusion.
Familiarity with reinforcement learning for planning and decision-making applications.
Understanding of secure coding practices and adversarial robustness in AI-driven systems.
Experience deploying AI models to embedded hardware such as Jetson, Raspberry Pi, or similar edge devices.
Exposure to simulation-in-loop and hardware-in-loop testing environments.
Knowledge of autonomous vehicle domains such as UAVs, USVs, and 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.

Responsibilities

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 and quantize language models and agent frameworks for deployment on edge-computing hardware, including Jetson and 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 such as ROS 2 to support mission execution across heterogeneous systems.
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 remain effective in denied, degraded, and contested communication environments.

Benefits

Competitive salary and comprehensive benefits.
Premium medical, dental, and vision coverage with zero paycheck premiums for the employee and family.
401(k) retirement savings plan with company match.
Equity grants for new hires.
Unlimited PTO.
11 paid holidays and an additional company holiday calendar, including a holiday hiatus in November and December.
Generous parental leave.
Military leave for annual training or service requirements.
FSA, DCFSA, and HSA options.
Professional development opportunities.
Access to One Medical.
Free 24/7 mental health resources, including therapy, and additional work-life services such as free legal and financial support.
Career growth opportunities.