Data Science - Agentic AI Intern
Who We Are
Cobalt was founded on the belief of a fundamental human aspiration: the desire to live better and safer. It all started in 2013, when our founders realized that pentesting can be better. Today our diverse, fully remote team is committed to helping organizations of all sizes with seamless, effective and collaborative Offensive Security Testing that empower organizations to OPERATE FEARLESSLY and INNOVATE 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 known as the Cobalt Core. Accepting just 5% of applicants, the Cobalt 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.
Cobalt is an Equal Opportunity Employer and we strive to build a diverse and inclusive workforce at our company. At Cobalt we aspire to engage with diverse individuals, communities, and organizations in order to continue to nurture our unique rich diverse culture. Join our team, and be your true self to do your best work.
Description
We are pushing the boundaries of agentic AI in the offensive security space, leveraging over 10 years of real-world offensive security 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 foundation 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 real-world environments. You will also develop models that capture complex system interactions and evaluate how agents perform 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 real-world environments
Analyze outputs from agents to identify system weaknesses and emergent behaviors
Collaborate with engineering and security teams to integrate models and agents into real-world offensive security workflows
Apply techniques from:
causal 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 empirical results and failure analysis
Communicate findings clearly to technical stakeholders
Must-Haves
Graduate student (MS/PhD) in a quantitative field
Strong foundation in at least one of: applied math, probability, statistics, and modeling
Proficiency in Python
Experience with agentic LLM-based AI systems
Nice-to-Haves
Familiarity with offensive security concepts
Experience with causal inference, simulation, or RL/planning
Research, publications, or open-source contributions
Why You Should Join Us
Ownership of a high-impact, technically challenging project
Exposure to real-world applications of agentic AI
Mentorship from experienced researchers and engineers
Opportunity to contribute to systems at the intersection of data science, AI, and security
Cobalt was founded on the belief of a fundamental human aspiration: the desire to live better and safer. It all started in 2013, when our founders realized that pentesting can be better. Today our diverse, fully remote team is committed to helping organizations of all sizes with seamless, effective and collaborative Offensive Security Testing that empower organizations to OPERATE FEARLESSLY and INNOVATE 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 known as the Cobalt Core. Accepting just 5% of applicants, the Cobalt 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.
Cobalt is an Equal Opportunity Employer and we strive to build a diverse and inclusive workforce at our company. At Cobalt we aspire to engage with diverse individuals, communities, and organizations in order to continue to nurture our unique rich diverse culture. Join our team, and be your true self to do your best work.
Description
We are pushing the boundaries of agentic AI in the offensive security space, leveraging over 10 years of real-world offensive security 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 foundation 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 real-world environments. You will also develop models that capture complex system interactions and evaluate how agents perform 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 real-world environments
Analyze outputs from agents to identify system weaknesses and emergent behaviors
Collaborate with engineering and security teams to integrate models and agents into real-world offensive security workflows
Apply techniques from:
causal 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 empirical results and failure analysis
Communicate findings clearly to technical stakeholders
Must-Haves
Graduate student (MS/PhD) in a quantitative field
Strong foundation in at least one of: applied math, probability, statistics, and modeling
Proficiency in Python
Experience with agentic LLM-based AI systems
Nice-to-Haves
Familiarity with offensive security concepts
Experience with causal inference, simulation, or RL/planning
Research, publications, or open-source contributions
Why You Should Join Us
Ownership of a high-impact, technically challenging project
Exposure to real-world applications of agentic AI
Mentorship from experienced researchers and engineers
Opportunity to contribute to systems at the intersection of data science, AI, and security