Red Teaming Team Lead
ActiveFence
Red Teaming Team Lead
- Intelligence
- Ramat Gan, IL
- Management
- Full-time
Description
ActiveFence is seeking an experienced and detail-oriented Red Teaming Team Lead to oversee complex research and delivery efforts focused on identifying and mitigating risks in Generative AI systems. In this role, you will lead a multidisciplinary team conducting adversarial testing, risk evaluations, and data-driven analyses that strengthen AI model safety and integrity.
You will be responsible for ensuring high-quality project delivery, from methodology design and execution to client communication and final approval of deliverables. This position combines hands-on red teaming expertise with operational leadership, strategic thinking, and client-facing collaboration.
Key Responsibilities
Operational and Quality Leadership
- Oversee the production of datasets, reports, and analyses related to AI safety and red teaming activities.
- Review and approve deliverables to ensure they meet quality, methodological, and ethical standards.
- Deliver final outputs to clients following approval and provide actionable insights that address key risks and vulnerabilities.
- Offer ongoing structured feedback on the quality of deliverables and the efficiency of team workflows, driving continuous improvement.
Methodology and Research Development
- Design and refine red teaming methodologies for new Responsible AI projects.
- Guide the development of adversarial testing strategies that target potential weaknesses in models across text, image, and multimodal systems.
- Support research initiatives aimed at identifying and mitigating emerging risks in Generative AI applications.
Client Engagement and Collaboration
- Attend client meetings to address broader methodological or operational questions.
- Represent the red teaming function in cross-departmental collaboration with other ActiveFence teams.
Requirements
Must Have
- Proven background in red teaming, AI safety research, or Responsible AI operations.
- Demonstrated experience managing complex projects or teams in a technical or analytical environment.
- Strong understanding of adversarial testing methods and model evaluation.
- Excellent communication skills in English, both written and verbal.
- Exceptional organizational ability and attention to detail, with experience balancing multiple priorities.
- Confidence in client-facing environments, including presenting deliverables and addressing high-level questions.
Nice to Have
- Advanced academic or research background in AI, computational social science, or information integrity.
- Experience authoring or co-authoring publications, white papers, or reports in the fields of AI Safety, Responsible AI, or AI Ethics.
- Engagement in professional or academic communities related to Responsible AI, trust and safety, or machine learning security.
- Participation in industry or academic conferences.
- Familiarity with developing or reviewing evaluation frameworks, benchmarking tools, or adversarial datasets for model safety testing.
- Proven ability to mentor researchers and foster professional development within technical teams.
- A proactive, research-driven mindset and a passion for ensuring safe, transparent, and ethical AI deployment.
About ActiveFence
ActiveFence is the leading provider of security and safety solutions for online experiences, safeguarding more than 3 billion users, top foundation models, and the world’s largest enterprises and tech platforms every day.
As a trusted ally to major technology firms and Fortune 500 brands that build user-generated and GenAI products, ActiveFence empowers security, AI, and policy teams with low-latency Real-Time Guardrails and a continuous Red Teaming program that pressure-tests systems with adversarial prompts and emerging threat techniques. Powered by deep threat intelligence, unmatched harmful-content detection, and coverage of 117+ languages, ActiveFence enables organizations to deliver engaging and trustworthy experiences at global scale while operating safely and responsibly across all threat landscapes.