Senior Research Scientist, Faculty

• A track record working with high-impact AI research, evidenced by top-tier academic publications (ideally in top machine learning or NLP conferences ACL/Neurips, ICML, ICLR, AAAI) or equivalent experience (e.g. within model providers labs)
• Deep domain knowledge in language models and AI safety, with the ability to contribute well-informed views about the differential value, and tractability, of different parts of the traditional AI Safety research agenda, or other areas of machine learning (e.g. explainability)
• Practical experience of machine learning, with a focus on areas such as robustness, explainability, or uncertainty estimation
• Advanced programming and mathematical skills with Python and an experience with the standard Python data science stack (NumPy, pandas, Scikit-learn etc.)
• The ability to conduct and oversee complex technical research projects
• A passion for leading and developing technical teams; adopting a caring attitude towards the personal and professional development of others
• Excellent verbal and written communication skills

Desirable

• As a Senior Research Scientist at Faculty you will contribute to Faculty’s success by performing novel scientific research in the area of AI safety that progresses scientific understanding though external publications and Faculty’s commercial ambition to build safe AI systems
• This is a great opportunity to join a small, high agency team of machine learning researchers and practitioners applying data science and machine learning to business problems in the real world
• Own and drive forward themes and areas of interest demonstrated by high-impact AI research
• Contribute to the wider vision for Faculty’s research effort in AI safety through team contributions to the research agenda
• Support Faculty’s positioning as a leader in AI safety through thought leadership and stakeholder engagement
• Shape our research agenda by identifying impactful research opportunities and balancing scientific and practical priorities
• Interface with the wider business to ensure alignment between the R&D team’s research efforts and the company's long term goals with a specific focus in the AI safety and commercial projects in the space
• Guide and mentor junior researchers in the team, fostering a collaborative and innovative culture across a wide-range of AI Safety-relevant research topics
• Lead technical research within the AI Safety space; some examples of AI safety research we’ve worked on include developing novel attacks against models, fine-tuning models for additional safety (e.g. knowledge unlearning), increasing self-consistency of language models and uncertainty estimation
• Support the delivery of evaluations and red-teaming projects in high-risk domains, such as CBRN and cybersecurity, with a focus on government and commercial partners