3 months ago
Washington, DC, USAIntern / Entry Level
Responsibilities
- Conduct adversarial testing of AI systems across safety, security, and misuse scenarios.
- Design and execute red teaming exercises against models, agents, and AI-enabled applications.
- Develop prompts, attack strategies, and test cases to identify vulnerabilities and failure modes.
- Analyze model behavior and document findings in clear, actionable reports.
- Support the development of evaluation frameworks, taxonomies, and testing methodologies.
- Research emerging AI threats, attack techniques, and risk trends.
- Collaborate with engineers, researchers, and subject-matter experts to investigate emerging risks and threat vectors.
- Develop front-end dashboards and other visualizations.
Requirements
- Pursuing or recently completed a degree in Computer Science, Cybersecurity, Engineering, Data Science, Mathematics, Political Science, International Relations, or a related field.
- Strong interest in AI safety, cybersecurity, red teaming, or adversarial testing.
- Familiarity with AI abuse including jailbreaking, system prompt extraction, indirect prompt injection, data exfiltration, etc.
- Strong analytical, research, problem-solving, and communication skills.
- Ability to think creatively from an adversarial perspective.
- Ability to work independently and collaboratively in a remote environment.
- Experience with Python, Bash, large language models, AI systems, cybersecurity, or research methodologies preferred.
- Proficiency in a language other than English preferred.
Benefits
- Flexible start / end dates.
- Remote work (based in the continental U.S.).
- Flexible schedule, up to 20 hours per week (negotiable).
- Hourly pay commensurate with experience and qualifications.
- $25 per hour for undergraduate students.
- $32.50 per hour for graduate students.
About 10a Labs
10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely.