Senior Responsible AI Researcher
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Job Description
Senior Responsible AI Researcher advancing frontier-model safety, evaluation, and adversarial robustness research. Building benchmarks and safeguards for Alice’s AI trust and security solutions.
Responsibilities:
- Lead state-of-the-art research initiatives at the intersection of frontier models, Trust & Safety, model evaluation, and adversarial robustness
- Drive self-initiated research and design evaluation methodologies for emerging risks including persuasion, manipulation, sycophancy, and agentic safety
- Build safety benchmarks across Generative AI modalities: Text, Image, Multimodal, and Autonomous Agents
- Serve as a key technical authority collaborating with AI labs, academic institutions, and enterprise partners
- Design, build, and deploy evaluation frameworks, benchmarks, and Agentic Safety Gyms
- Develop adversarial prompting methodologies, red-teaming strategies, and behavioral suites to stress-test LLMs, Multimodal systems, and AI Agents
- Investigate emerging safety risks including bias and jailbreaking tactics across multimodal and autonomous architectures
- Lead collaborative research projects with academic institutions and industry partners
- Represent the organization at international conferences, AI safety summits, and industry events
- Publish research findings in top AI and safety venues
- Partner with Product, Engineering, Go-To-Market, Policy, and Delivery teams to translate research into safety products and client-facing offerings
- Engage technical stakeholders and enterprise clients to translate safety and governance requirements into research questions, experiments, and evaluation suites
- Provide technical leadership and mentorship to junior researchers, data scientists, and engineers
- Integrate social science, policy, and technical perspectives into safety tooling and methodologies
Requirements:
- PhD in Science, Technology and Society Studies (STS), Machine Learning, AI Ethics, Social Data Science, or a closely related research domain
- 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)
- Substantial experience in AI Safety, Responsible AI (RAI), Trust & Safety, or Model Evaluation
- Strong hands-on capabilities in Python and modern ML/DL frameworks (e.g., PyTorch, Transformers)
- Deep working knowledge of foundation models, including LLMs, Text-to-Image, Multimodal systems, agents, reasoning systems, and current safety mitigation techniques
- Proven experience designing datasets, benchmark suites, red-teaming protocols, or synthetic simulation platforms
- Proven track record of self-initiating research projects, establishing external partnerships, and shaping technical vision
- Excellent verbal and written communication skills, including communicating complex technical concepts to non-technical stakeholders, executives, and research peers
- Ability to work across technical data science, social science paradigms, and product execution
- Prior employment, consulting, advisory, or formal research collaboration with frontier AI labs or leading safety research collectives (preferred/desirable)
- Experience in domain-specific safety applications such as election integrity, psychological manipulation, or agentic behavioral safety suites (preferred/desirable)
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