Applied AI Research Engineer

An incomplete list of ongoing and near-term directions:

Human-machine teaming for AI-assisted course-of-action development, comparison, critique, refinement, and operational decision support

Agentic planning systems that integrate language models with simulation, doctrine retrieval, external tools, structured outputs, and deterministic verification

Adapting and optimizing foundation models through fine-tuning, post-training, distillation, reinforcement learning, and rigorous evaluation for planning and decision-support tasks

Multi-agent AI systems for Red/Blue planning, control-cell support, adjudication, branch-and-sequel analysis, and collaborative planning workflows

Building reliable AI systems using self-correction, structured reasoning, constraint-aware generation, verification, and robust tool use

Learning from human expertise through planner feedback, preferences, approvals, synthetic data generation, and human-in-the-loop improvement

Trustworthy AI for high-consequence applications, with an emphasis on explainability, provenance, traceability, auditability, uncertainty estimation, and model behavior analysis

Design and build agentic AI systems for planning, decision support, and human-machine teaming

Develop AI pipelines that integrate foundation models, retrieval, simulation, external tools, and deterministic software

Design, run, and analyze experiments to evaluate model and agent performance, reliability, traceability, latency, cost, and user trust

Fine-tune, distill, and evaluate foundation models for domain-specific planning, reasoning, and decision-support tasks

Build datasets, retrieval pipelines, automated benchmarks, and experiment infrastructure to support continuous model improvement and reproducible research

Partner with software engineers to transition research prototypes into scalable AI services

Collaborate with domain experts to translate operational workflows into AI-enabled capabilities while ensuring AI outputs remain explainable, reviewable, and under human control

Mission with impact: Build AI systems that help users reason through high-consequence operational decisions.

AI beyond demos: Work on systems where models are paired with software, verification, simulation, guardrails, and human oversight.

Greenfield research: Explore ambitious ideas in GenAI, RL, agentic workflows, evaluation, and human-machine teaming.

Small-team velocity: Move quickly from research question to prototype to user-facing capability.

Real users: See your work tested by planners, analysts, engineers, and operational stakeholders.