AI Agentics Scientist Computer Engineer Autonomous Orchestration Generative Systems

Air Force Research Laboratory; City of Rome (NY); On-site; USD 70,000 - 95,000; Full time

Job summary

Air Force Research Laboratory seeks entry-level scientists (GS-07 to GS-11) in Computer Science/Data Science or Computer Engineering who specialize in AI agentics, foundation models, and autonomous orchestration.

The role bridges academic AI research with critical C3 operations, exploring multi-agent reasoning, adversarial AI defenses, synthetic data, and high-fidelity simulations for warfighter decision advantage.

Qualifications

• Advanced degree in a quantitative field with a strong research focus on AI/ML autonomous agents.
• Experience or coursework in multi-agent systems and decision-making under uncertainty.
• Knowledge of safety, robustness, and evaluation methods for agentic AI in complex environments.

Responsibilities

• Design, prototype, and evaluate autonomous multi-agent architectures and reasoning paradigms.
• Research defenses against adversarial vulnerabilities in generative/agentic systems.
• Develop synthetic data and simulation environments to benchmark autonomous workflows.

Skills

AI/ML Autonomous Agents

Multi-Agent Reinforcement Learning

Generative AI

Natural Language Reasoning

Education

Master’s or Ph.D. in Computer Science/Data Science/Aerospace/Systems Engineering

Tools

LangGraph

AutoGen

CrewAI

Semantic Kernel

DSPy

vLLM/TensorRT-LLM

LoRA/PEFT

Vector Databases

Retrieval-Augmented Generation (RAG)

Job description

The Information and Spectrum Warfare Directorate (RF), Information Systems Division (RFC) is seeking an entry-level, DR-01 (GS-07 to GS-11 or equivalent) Computer Scientist, Data Scientist, or Computer Engineer specializing in AI Agentics, Foundation Models, and Autonomous Orchestration to bridge academic AI research with critical operational needs across Command, Control, and Communications (C3). This role will investigate open research questions in autonomous multi-agent reasoning, counterfactual explorations, adversarial and system-of-systems AI/ML, synthetic data/environment generation, and goal-driven decision autonomy to deliver resilient decision advantage to warfighters operating in complex information and contested environments.

Position duties include, but are not limited to:

Research and development of Agentic Orchestration Architectures: Design, prototype, and evaluate autonomous multi-agent frameworks, reasoning paradigms (e.g., ReAct, chain/tree-of-thought, reflection loops), and tool-augmented foundation models to automate complex information triage, dynamic task decomposition, and cooperative battlespace decision workflows.

Investigate Adversarial AI & Agent Robustness: Formulate, implement, and benchmark defenses against adversarial vulnerabilities in generative and agentic systems, including prompt injection, jailbreaking, data poisoning, adversarial tool misuse, and non-deterministic hallucination, ensuring deterministic reliability in safety-critical C3 environments.

Generate High-Fidelity Synthetic Data & Simulation Environments: Construct generative models, probabilistic world models, and simulated multi-agent sandboxes to synthesize diverse training corpora, stress-test tactical decision workflows, and benchmark autonomous agent performance under simulated Contested, Degraded, and Operationally-Limited (CDO-L) scenarios.

Scientific Experimentation: Apply R&D benchmarking to evaluate agentic metrics (e.g. agent convergence, latency, explainability, and task accuracy); collaborate with partnered military and academia organizations; assist in authoring of technical reports, conference papers, and assist in briefings and demonstrations.

Qualifications: US Citizenship

Ability to obtain/maintain a Top Secret clearance as a condition of employment.

Males must be registered for Selective Service, see www.sss.gov.

This is a drug testing designated position. The incumbent will be subject to random testing.

Applicants must be able to meet the OPM Qualification Standards of the position being filled: possess a professional Science or Engineering (S&E) degree, in a relevant technical field, from an ABET accredited academic institution.

This is an Acquisition Professional Development Program (APDP) position requiring Level/Tier 1 (Intermediate) certification in the Engineering & Technical Management stall. Applicants must list all completed APDP certifications on resume. Selectee will have 5 years to meet the APDP certification requirements of the position.

Benefits:

Yes

Skills required: Advanced degree (Master’s or Ph.D.) in Computer Science, Data Science, Aerospace/Systems Engineering, or a related quantitative field, with a strong research focus on AI/ML Autonomous Agents, Multi-Agent Reinforcement Learning (MARL), Generative AI, or Natural Language Reasoning.

Familiarity with agentic frameworks and orchestration concepts (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, DSPy, or custom tool-use architectures.)

Hands-on experience with modern model optimization, inference acceleration, and context management techniques (e.g., vLLM, TensorRT-LLM, LoRA/PEFT, Vector Databases, and Retrieval-Augmented Generation [RAG]).

Experience in synthetic data generation methods (e.g., diffusion models, GANs, LLM-driven data generation, or physics/game-engine simulation sandboxes like Unity, Isaac Sim, or discrete-event simulators).

Familiarity with techniques for model optimization, inference acceleration, and context management (e.g., vLLM, TensorRT-LLM, LoRA/PEFT, vector databases, retrieval-augmented generation [RAG]).