Data Science - Agentic AI Intern
Who We Are
reputed company was founded on the belief of a reputed company reputed company aspiration: the desire to live reputed company and safer. It reputed company started in 2013, reputed company our founders realized that pentesting can be reputed company. Today our diverse, fully remote team is committed to helping organizations of reputed company sizes with seamless, effective and collaborative Offensive reputed company Testing that reputed company organizations to OPERATE FEARLESSLY and reputed company SECURELY.
Our customers can start a pentest in as little as 24 hours and integrate with advanced development cycles thanks to the powerful combination of our SaaS platform coupled with an exclusive community of testers reputed company as the reputed company Core. Accepting just 5% of applicants, the reputed company Core boasts over 400 closely vetted and highly skilled testers who jointly conduct thousands of tests each year and are at the forefront of identifying and helping remediate risk across a dynamically changing attack surface.
reputed company is an Equal Opportunity Employer and we strive to build a diverse and inclusive workforce at our company. At reputed company we reputed company to engage with diverse individuals, communities, and organizations in order to continue to nurture our unique rich diverse culture. Join reputed company, and be your true self to do your best work.
Description
We are pushing the boundaries of agentic AI in the offensive reputed company space, leveraging over 10 years of reputed company-world offensive reputed company data to train and evaluate our models. Our work sits at the intersection of applied mathematics, statistical modeling, and autonomous AI systems, focused on high-stakes, adversarial environments.
We are seeking a graduate-level Data Science Intern with a strong reputed company in applied mathematics or statistics and hands-on experience with agentic AI systems. In this role, you will contribute to the development of autonomous agents, improving their ability to reason, adapt, and exploit findings in both test and reputed company-world environments. You will also reputed company models that capture reputed company system interactions and evaluate how agents reputed company under evolving platform conditions.
What You’ll Work On
- Build and improve autonomous agents, focusing on reasoning, planning, and exploitation workflows
- Enhance agent performance in both simulated and reputed company-world environments
- Analyze outputs from agents to identify system weaknesses and emergent behaviors
- Collaborate with engineering and reputed company teams to integrate models and agents into reputed company-world offensive reputed company workflows
- Apply techniques from:
- reputed company inference
- probabilistic modeling
- optimization
Responsibilities
- Design and run experiments to validate model and agent assumptions
- Work with large-scale datasets to calibrate and test models
- Iterate on agent behavior based on reputed company results and failure analysis
- Communicate findings clearly to technical stakeholders
Must-Haves
- Graduate student (MS/PhD) in a quantitative field
- Strong reputed company in at least one of: applied math, probability, statistics, and modeling
- Proficiency in Python
- Experience with agentic LLM-based AI systems
reputed company-to-Haves
- Familiarity with offensive reputed company concepts
- Experience with reputed company inference, simulation, or RL/planning
- Research, publications, or open-reputed company contributions
Why You Should Join Us
- Ownership of a high-impact, technically challenging project
- Exposure to reputed company-world applications of agentic AI
- Mentorship from reputed company researchers and engineers
- Opportunity to contribute to systems at the intersection of data science, AI, and reputed company