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Applied AI Data Engineer

Applied AI Data Engineer

We are a small, mission-focused U.S. defense contractor building advanced analytical capabilities that combine AI, automation, and data-driven decision support. This role is for an Applied AI Data Engineer who can turn ambiguous questions into defensible data assets, modeling workflows, and repeatable evaluation methods. You will work closely with leadership and staff to help translate research-grade ideas into reliable capabilities.

Responsibilities

Design and build data pipelines that ingest, clean, normalize, and structure heterogeneous data for downstream modeling and analytical workflows.
Develop reusable datasets, features, labels, and representations from both structured and unstructured sources.
Create and refine methods for segmentation, cohorting, calibration, and scenario-based model evaluation.
Apply machine learning, NLP, retrieval, embeddings, and related methods to extract signal from noisy real-world data.
Design benchmarks and validation procedures that measure accuracy, stability, uncertainty, bias, and performance across cohorts and scenarios.
Prototype simulation-oriented or predictive modeling approaches and iterate based on quantitative results and error analysis.
Document assumptions, experiment design, data lineage, and model limitations clearly for technical and non-technical stakeholders.
Operationalize research and modeling outputs into secure, maintainable internal tools.

Core Qualifications

U.S. citizenship required.
Education: Bachelor’s or Master’s degree in computer science, data science, applied mathematics, statistics, engineering, or a closely related field.
Strong Python skills and sound software engineering habits.
Hands-on experience with applied machine learning, data science, NLP, information retrieval, or model evaluation work.
Experience using Python-based tools such as Pandas, PyTorch, PostgreSQL, Hugging Face, sentence transformers, or similar technologies.
Ability to work with incomplete, messy, or high-variance datasets and turn them into reliable inputs for analysis and model training.
Experience designing experiments and evaluation plans, then iterating from measured results.
Comfort working in Docker- and Git-based development environments and collaborating closely with engineers building production systems.
Strong written communication, attention to detail, and the ability to operate with autonomy in a lean team.

Preferred Background

Experience with modeling, simulation, probabilistic methods, forecasting, or other techniques for studying complex systems.
Experience integrating demographic, geographic, survey, event, economic, or other population-scale datasets into analytical workflows.
Familiarity with retrieval systems, vector databases, semantic search, graph-based retrieval, or graph/RAG experimentation.
Exposure to model calibration, sensitivity analysis, fairness assessment, and representativeness challenges in real-world data.
Academic, laboratory, or research experience in computer science, data science, applied statistics, computational social science, or a related quantitative field.

To Apply: