Senior Researcher in Interpretability and AI Safety

Location: Central Oxford

We are seeking a full time Senior Researcher to join the Technical AI Governance programme at the Department of Engineering Science in central Oxford. The post is funded by the Oxford Martin AI Governance Initiative and is fixed-term to 1 year, with the possibility of an extension for an additional year.

The Senior Researcher will work on Interpretability, evaluations and AI safety for continuously learning systems. They will evaluate systems whose behaviour keeps moving, and track, from the inside, whether the structures that carry their capabilities and safety properties hold up under the change. 

You will be responsible for the follow: (full details of duties available from the Job Description)

Research

Collaboration and engagement

You will have completed a PhD in machine learning, computer science, or a closely related field and have research experience in at least one of: mechanistic or representational interpretability; model evaluation and benchmarking; continual or lifelong learning; or AI safety (alignment, scalable oversight, adversarial robustness). A strong publication record at relevant venues (NeurIPS, ICML, ICLR, ACL, EMNLP, or established AI safety venues and workshops) together with the ability to design, run, and make sense of large-scale experiments on foundation models. 

Informal enquiries may be addressed to Fazl Barez at fazl.barez@eng.ox.ac.uk. 

For more information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us/ 

Only online applications received before midday on 22 September 2026 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application.

The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.

AI, interpretability, continuous learning, evaluations chain-of-thought. 

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