Applied AI Research Engineer

About the role

We're building next-generation AI systems that help military planners explore, compare, and evaluate operational courses of action. Our work combines frontier language models, simulation, planning, and verification into human-in-the-loop decision-support systems for defense applications.

Responsibilities

• Focus on human machine teaming and agentic AI to build systems that allow warfighters, planners, analysts, and decision-makers to explore operational choices with speed, confidence, and control.
• Design and build agentic AI systems – not chatbots.
• Develop multi-agent workflows, fine-tune and evaluate models, build retrieval pipelines, experiment with post-training techniques, and integrate AI with simulation and planning software.
• Work closely with AI researchers, software engineers, and defense experts to turn research ideas into production-ready capabilities.
• Research areas of interest include 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.

Requirements

• Bachelor's or Master's degree in Computer Science, Machine Learning, Engineering, Mathematics, Physics, or a related technical field, or equivalent practical experience.
• 3+ years building AI, machine learning, or applied research systems.
• Strong Python engineering skills.
• Experience with PyTorch and modern LLM tooling (Transformers, vLLM, Hugging Face, etc.).
• Experience building or deploying agentic AI systems, tool-calling workflows, or multi-step reasoning pipelines.
• Experience fine-tuning, evaluating, or serving language models.
• Experience with retrieval-augmented generation, embeddings, vector search, or knowledge retrieval systems.
• Strong understanding of experiment design, benchmarking, and model evaluation.
• Ability to move quickly from research prototype to production-quality implementation.

Qualifications

• Eligible to obtain a U.S. security clearance.

Skills

• Experience with agentic AI systems, tool-calling workflows, or multi-step reasoning pipelines.
• Experience fine-tuning, evaluating, or serving language models.
• Experience with retrieval-augmented generation, embeddings, vector search, or knowledge retrieval systems.
• Strong understanding of experiment design, benchmarking, and model evaluation.
• Ability to move quickly from research prototype to production-quality implementation.

Benefits

• Pay depends on experience, but we strive to be at the upper end of the salary range.
• Health care plan with 100% premium coverage, including medical, dental, and vision.
• 401k with 5% matching.
• Paid Time Off (uncapped vacation, plus sick and public holidays).
• Flexible hybrid or remote work arrangement.
• Relocation assistance for qualifying employees.

Pay

Pay depends on experience, but we strive to be at the upper end of the salary range.