AI/ML Engineer

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 (ROS 2, navigation stacks, 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 (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