Senior Responsible AI Researcher

We are seeking a visionary, highly accomplished Senior Responsible AI Researcher to lead state-of-the-art research initiatives at the intersection of frontier models, Trust & Safety (T&S), model evaluation, and adversarial robustness.

In this role, you will bridge deep academic rigor with practical, real-world AI safety challenges. You will drive self-initiated research, design evaluation methodologies for emerging risks (e.g., persuasion, manipulation, sycophancy, agentic safety), and build safety benchmarks across Generative AI modalities (Text, Image, Multimodal, and Autonomous Agents). You will also serve as a key technical authority collaborating directly with top-tier AI labs, academic institutions, and enterprise partners.

Key Responsibilities

1. Frontier AI Safety & Evaluation Research

• Design, build, and deploy new evaluation frameworks, benchmarks, and "Agentic Safety Gyms" to measure model capabilities, vulnerabilities, and alignment across diverse risk domains (e.g., Hate Speech, Misinformation, Elections Preparedness, IP/Copyright, Child Safety).
• Develop advanced adversarial prompting methodologies, red-teaming strategies, and behavioral suites to stress-test Large Language Models (LLMs), Multimodal systems, and AI Agents.
• Investigate emerging safety risks, including model persuasion, manipulation, sycophancy, bias, and jailbreaking tactics across multimodal and autonomous architectures.

2. Research Leadership & Outbound Impact

• Lead high-impact collaborative research projects with external academic institutions (e.g., Oxford, Harvard, MATS, GovAI) and industry partners (e.g., OpenAI, Google DeepMind, Anthropic, Microsoft, Nvidia).
• Represent the organization as a thought leader and speaker at top-tier international conferences, technical AI safety summits, and industry events.
• Publish groundbreaking research findings in top AI and safety venues.

3. Strategic Client & Cross-Functional Collaboration

• Partner closely with Product, Engineering, Go-To-Market (GTM), Policy and Delivery teams to translate research discoveries into scalable safety products, client-facing offerings, and delivery methodologies.
• Engage directly with technical stakeholders and enterprise clients to translate complex safety and governance requirements into actionable research questions, experiments, and evaluation suites.

4. Mentorship & Team Augmentation

• Provide technical leadership and mentorship to junior researchers, data scientists, and engineers.
• Bridge interdisciplinary gaps by integrating social science, policy, and technical perspectives into robust safety tooling and methodologies.

Must Haves:

Education & Track Record

• PhD in Science, Technology and Society Studies (STS), Machine Learning, AI Ethics, Social Data Science or a closely related research domain.
• Proven Research Record: Demonstrated academic excellence with a strong portfolio of peer-reviewed publications and conference presentations at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, IEEE, CVPR, or dedicated AI Safety conferences).

Technical & Domain Expertise

• Deep Domain Expertise: Substantial experience in AI Safety, Responsible AI (RAI), Trust & Safety, or Model Evaluation.
• Technical Mastery: Strong hands-on capabilities in Python and modern ML/DL frameworks (e.g., PyTorch, Transformers).
• Generative AI & Agentic Systems: Deep working knowledge of foundation models (LLMs, Text-to-Image, Multimodal), agents, reasoning systems, and current safety mitigation techniques.
• Benchmarking & Methodology: Proven experience in designing datasets, benchmark suites, red-teaming protocols, or synthetic simulation platforms.

Mindset & Soft Skills

• Ownership & Self-Starter Attitude: Proven track record of self-initiating research projects, bringing in external partnerships, and shaping technical vision.
• Communication & Engagement: Excellent verbal and written communication skills with the ability to convey complex technical concepts to non-technical stakeholders, executives, and research peers.
• Cross-Disciplinary Versatility: Ability to operate comfortably across technical data science, social science paradigms, and product execution.

Preferred / Desirable Qualifications

• Prior employment, consulting, advisory, or formal research collaboration with frontier AI labs (e.g., OpenAI, Anthropic, Google DeepMind, Meta AI) or leading safety research collectives (e.g., MATS, GovAI).
• Experience in domain-specific safety applications, such as election integrity, psychological manipulation, or agentic behavioral safety suites.