AI / ML Engineer

DESE Research, Inc.•Huntsville, AL

About The Position

DESE's Cyber Works and Digital Engineering teams design, build, and integrate emerging AI/ML technologies to harden and secure the systems that defend the nation, across ground, missile defense, space, and installation infrastructure S&T programs. We're expanding our Secure AI practice to build and assure trusted, robust AI/ML solutions that interpret complex datasets, predict outcomes, and automate decision-making in support of critical military platforms. This is a consolidated announcement covering multiple tracks; your assignment may emphasize one or a blend of: Classic ML & Predictive Modeling, LLM/GenAI Applications, AI Assurance & Responsible AI, Edge AI/ML Deployment, and hardening AI/ML systems against adversarial threats. All tracks require independent research, cross-functional collaboration, and the ability to clearly communicate complex technical work to stakeholders.

Requirements

Bachelor's degree or equivalent experience in CS/CPE/EE/Data Science (or related field).
Proven experience in one or more: ML/LLM development, assurance/evaluation tooling, or deploying edge computing solutions.
Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Proficiency in Python and at least one additional language (Java, C++).
Strong understanding of data structures, data modeling, and software architecture.

Nice To Haves

Modern AI: LLM frameworks and application development; vector embeddings and vector databases; RAG architectures; prompt/context engineering; LLM fine-tuning; model evaluation and benchmarking.
Classic ML: Feature engineering, model selection/tuning, statistical analysis, and predictive modeling across structured and unstructured data.
AI Assurance: Responsible AI practices (robustness, bias/fairness, traceability); adversarial ML defenses; red-team testing; auditability and compliance documentation.
Edge & Deployment: ONNX/TensorRT, quantization/compression, ARM/NVIDIA Jetson/DSP targets, containerized inference, MLOps in controlled/classified environments.
Platform & DevSecOps: REST APIs, CI/CD for software and ML systems, SAST/DAST, Infrastructure-as-Code, cloud platforms (AWS, Azure, GCP).
Domain: Familiarity with computer networking, secure system integration for mission platforms, and test/V&V support (Python/MATLAB analysis).
Contributions to open-source AI/ML projects; experience deploying AI models in production, classified, or embedded environments.
Understanding of computer security principles and secure software development lifecycle (SSDLC) practices.

Responsibilities

Design, develop, and maintain machine learning models, from classical algorithms (regression, tree ensembles, clustering) to modern deep learning architectures.
Build LLM-enabled applications and retrieval-augmented generation (RAG) pipelines, including vector embedding generation, vector database integration, and prompt/context engineering.
Develop AI assurance and evaluation tooling: robustness testing, bias/fairness analysis, model traceability, red-team/adversarial testing, and audit artifact generation.
Optimize and deploy models for production and edge environments (quantization, compression, containerized inference, ONNX/TensorRT).
Implement secure model and data pipelines, defend against adversarial ML threats, and ensure supply-chain integrity (SBOM) for AI components.
Conduct data processing/analysis to improve model accuracy; document and present development processes and assurance evidence to stakeholders.
Contribute to Agile, team-based planning and estimating in a fast-paced, collaborative environment.