GenAI Safety Team Lead

🏢 ActiveFence · all ActiveFence jobs
📍 United States
💰 USD 127,000 - 140,000 / annual
📅 Posted 2026-07-30 · via Himalayas
🏷 AI-Safety,Red-Teaming,Trust-and-Safety,Research-Leadership,AI-Security,Director-Of-AI-Safety,GenAI-Leadership
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Description

Alice is seeking an experienced and detail-oriented GenAI Safety 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 and teams in a technical or analytical environment.

- 5+ years of experience

- 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.

The salary range for this role is $127K - $140K OTE - Range may vary based on experience. Salary at the time of offer will be commensurate with experience.
About Alice

Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact—whether with each other or with machines.

In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.

Originally posted on Himalayas

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