Support Federal Law Enforcement missions by applying technical expertise to complex investigative and operational challenges.
Locations (8)
Merrifield, Virginia, United States
Crystal City, Virginia, United States
Washington, District of Columbia, United States
McLean, Virginia, United States
New York City, New York, United States
San Francisco, California, United States
Show all 8 locations
Los Angeles, California, United States
Irvine, California, United States
Compensation
The base compensation range for the role in any of those locations is $117,700 USD - $218,600 USD. For any eligible US locations, unless otherwise noted, the base compensation range for this role is $102,200 USD - $189,800 USD.
Responsibilities
Collect, integrate, clean, verify, resolve, and maintain large and complex datasets.
Analyze data using statistical methods, graph networks, anomaly detection, spatial analysis, pattern-of-life analysis, sentiment analysis, and related techniques.
Develop and apply data models, including risk models, community detection, classification, computer vision, machine learning, and artificial intelligence models.
Translate data findings into actionable insights through dashboards, visualizations, briefings, and clear explanations for technical and non-technical audiences.
Write, edit, customize, and execute Python code to support mission requirements.
Requirements
Bachelor’s or Master’s degree in a technical field such as data science, statistics, computer science, mathematics, physical or biological sciences, geographic information systems, or a related discipline
This internship focuses on machine learning surrogate models for engineering simulation and measurement. The intern will design, train, and evaluate neural surrogate models to predict engineering responses from simulation or measurement data faster than repeated high-fidelity simulation.
Compensation
(PhD) $51-$56
Responsibilities
Generate parametric engineering datasets using simulation and/or measurement sources, with attention to sampling strategy and to numerical noise in the underlying data.
Design, train, and evaluate neural surrogate models for engineering response prediction.
Investigate how the choice of output representation affects accuracy, particularly across operating regions or frequency ranges where the response varies sharply.
Establish rigorous baselines and quantify accuracy as a function of simulation or measurement budget.
Evaluate LoRA and related parameter-efficient methods for specializing trained models to new problem settings, against full and partial fine-tuning.
