AI & Data Science Intern

GuidePoint Security provides trusted cybersecurity expertise, solutions and services that help organizations make better decisions and minimize risk. By taking a three-tiered, holistic approach for evaluating security posture and ecosystems, GuidePoint enables some of the nation’s top organizations, such as Fortune 500 companies and U.S. government agencies, to identify threats, optimize resources and integrate best-fit solutions that mitigate risk.

Overview

As part of the GPSU program, we are seeking an AI & Data Science Intern to support GuidePoint Security’s Northeast region.  In this role, you will work with the NE AI and AppSec teams as part of the NE DevSecOps team, contributing to the evaluation and governance infrastructure that supports GuidePoint’s agentic AI harnesses. This is a hands-on, practitioner-mentored role focused on defining and running evals, collecting and analyzing telemetry, tuning model performance, and building an internal governance pipeline for AI harness outputs. Prior professional experience is not required, but a data-oriented mindset and curiosity about applied AI systems are essential.

Currently enrolled, or recently graduated with a degree or substantial coursework in data science, statistics, computer science, engineering, or a related field is required. You will contribute to live AI systems that are in active use across the organization, working alongside experienced engineers to improve harness reliability and prevent output drift and performance regression over time. You will also participate in limited client-facing engagements under mentorship, observing how AI capabilities are communicated and demonstrated in a professional services context.

Responsibilities

• Evaluation & Performance

• Define and implement evaluation frameworks to measure the accuracy, reliability, and consistency of agentic AI harnesses

• Design and run structured evals against harness outputs to detect regressions, output drift, and behavioral degradation over time

• Build and maintain benchmark datasets and ground-truth corpora used to evaluate harness quality

• Analyze eval results, identify failure modes, and collaborate with the NE AI and AppSec teams to drive iterative improvements

• Research and apply prompt evaluation techniques and LLM benchmarking methodologies relevant to security-specific harnesses

• Telemetry, Tuning & Governance

• Instrument agentic harnesses with telemetry to capture latency, token usage, error rates, and output quality signals

• Analyze telemetry data to identify patterns, surface anomalies, and generate actionable insights for harness tuning