AI/ML Engineer

Noda AI

$90K — $130K *
Aerospace & Defense
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 3+ years of experience in production AI/ML applications tied to LLM orchestration.
  • Proficiency in Python and AI/ML frameworks like PyTorch and Transformers.
  • Experience in optimizing AI models for edge deployment.
  • Understanding of real-time AI inference and distributed systems.
  • Familiarity with MLOps practices for AI lifecycle management.
  • Knowledge in prompt engineering and multi-step reasoning systems.
  • Experience with planning algorithms and constraint solving.

Responsibilities

  • Design and implement LLM orchestration frameworks for diverse vehicle fleets.
  • Develop agent reasoning systems that align mission goals with autonomous commands.
  • Optimize AI models for efficient operation on edge computing hardware.
  • Manage the complete lifecycle of AI agents including tool integration.
  • Implement workflows for explainable AI to enhance operator transparency.
  • Integrate AI outputs with autonomy middleware like ROS 2 for mission execution.
  • Build systems for monitoring agent performance in real-world scenarios.

Benefits

  • Hybrid work environment
  • Flexible time off
  • Generous PTO policy
  • Federal holidays
  • Comprehensive health, dental, and vision insurance benefits
  • Free OneMedical membership
Full Job Description
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

The Role

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.

Key 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 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
Required Qualifications
  • 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, 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 with the ability to obtain a security clearance
Preferred Qualifications
  • Experience with multi-agent coordination frameworks and distributed AI reasoning systems
  • Background in robotics or autonomous systems integration (ROS2ROS 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
Skills and Attributes
  • 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 mission-critical environments
  • Thrives in ambiguous, mission-driven problem spaces requiring creative AI solutions
  • Collaborative team player comfortable working across AI, autonomy, and field operations disciplines
  • Detail-oriented approach to AI system validation, testing, and performance optimization 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 mission-critical environments
  • Thrives in ambiguous, mission-driven problem spaces requiring creative AI solutions
  • Collaborative team player comfortable working across AI, autonomy, and field operations disciplines
  • Detail-oriented approach to AI system validation, testing, and performance optimization


What We Offer
  • Hybrid work environment
  • Competitive pay
  • Flexible time off
  • Generous PTO policy
  • Federal holidays
  • Generous health, dental, and vision benefits insurance
  • Free OneMedical membership
Growth Path at NODA

AI/ML Engineer 13 Senior AI/ML Engineer 13 Staff AI/ML Engineer 13 Principal Engineer. Final leveling will be determined at the offer stage based on scope demonstrated in interviews, prior impact, and calibration to NODA's career ladder.

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