AI & Data Science Intern

Be physically located within the United States during the entire duration of the internship. This position is not available for candidates living in the following states: Alaska, California, Hawaii, New Mexico, New York, Oregon, Washington and Wyoming., Eastern/Central time zone preferred; supporting the Eastern Time Zone.Part-TimeEntry

Salary20 USD per hour

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Job Details

Experience: Prior professional experience is not required.
Required Skills: PythonNumpyData sciencePandas

Requirements

Be currently enrolled in or recently graduated with a degree or substantial coursework in data science, statistics, computer science, engineering, or a related field.
Have proficiency or a strong interest in Python.
Have exposure to data manipulation libraries such as pandas or numpy, or to LLM APIs.
Bring curiosity about large language models, agentic systems, and applied AI performance and failure modes.
Have analytical and written communication skills to translate data findings into clear, actionable observations.
Have technical aptitude and comfort working with ambiguity.
Be available part-time for 25–29 hours per week, Monday through Friday, supporting the Eastern Time Zone.
Have a personal computing device and access to high-speed internet during working hours.

Responsibilities

Define and implement evaluation frameworks for agentic AI harness accuracy, reliability, and consistency.
Design and run structured evaluations to detect regressions, output drift, and behavioral degradation.
Build and maintain benchmark datasets and ground-truth corpora.
Analyze evaluation results, identify failure modes, and collaborate with the NE AI and AppSec teams on improvements.
Research and apply prompt evaluation techniques and LLM benchmarking methodologies for security-specific harnesses.
Instrument agentic harnesses to capture latency, token usage, error rates, and output quality signals.
Analyze telemetry data to identify patterns, surface anomalies, and inform harness tuning.
Support model and prompt tuning based on evaluation and telemetry findings; document changes and measure their impact.
Contribute to an internal AI governance pipeline by defining standards, logging outputs, flagging quality issues, and maintaining an audit trail.
Document governance processes, evaluation methodologies, and tuning decisions in a repeatable, version-controlled format.

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