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Senior Research Scientist for Foundational AI Safety - AI Safety Institute

Department for Science Innovation and Technology

Job Grade

Other (See Full Job Details)

Advertised Salary

£105,000 - £135,000

Type of Role

Other (See Full Job Details)

Closing Date

28. august 2024

Job Description from Civil Service Jobs

About the Role

As a Senior Research Scientist at AISI, you will lead Research Scientists and Research Engineers in planning and executing on research directions in foundational AI safety. You’ll lead and contribute to projects aimed at improving the fundamental safety of advanced AI systems, making novel research contributions that are aimed at informing governance with technical tools on safe AI.

We draw on a wide range of disciplines, and value a diversity of research expertise across our four workstreams. You’ll be primarily associated with one of our research workstreams (please specify in your application which you’re most interested in, details below), but sometimes your work will intersect multiple workstreams:

Capabilities elicitation & jailbreaking. This workstream consists of research to ensure that AISI can get closer to the “capabilities ceiling” of the models we work with, including development of novel jailbreaks and methods such as “next-gen chain-of-thought" to increase model performance on our evals.
AI model explainability & accountability. This workstream focuses on techniques for improving the transparency of model behaviour via e.g. casual reasoning traces.
Interventions on model behaviour. This workstream will investigate ways of intervening on model behaviour pre- and post-deployment to solve safety issues beyond anecdotal patching, i.e. in ways that will be robust to improved prompting techniques. E.g. via data attribution techniques.
Rethinking alignment. This workstream consists of developing methods for alignment that go beyond existing state-of-the-art, for example by reliance on formal languages or probabilistic approaches.
You will work under Prof. Yarin Gal, and work closely with your team comprising of multiple Research Scientists, Research Engineers, and Software Engineers, as well as benefit from support from our cross-functional Platform Engineering team. You’ll also collaborate with external topic-level experts, contractors, partner organisations and policy makers to coordinate and build on external research.

Day-to-day, you may:

Conduct novel research
Supervise Research Scientists in your team and mentor them
Design experiments to validate hypotheses about models’ behaviour
Work with experts to assemble relevant literature or datasets
Prepare papers for internal or external publication
Manage external research collaborations
Prepare a write-up of your research to brief policy makers
The ideal candidate will have the skills and experience that allow them to additionally:

Design experiments to conduct rigorous and impactful machine learning research
Analyse and interpret data in ways that meaningfully contribute to research at AISI
Understand the capabilities and potential impact of advanced AI systems, including large language models
Keep track of and respond to the fast-moving literature, ensuring that our research is additional to that being conducted elsewhere
Coordinate closely with research and engineering partners in your team and stakeholders across AISI to design new methods
Clearly communicate insights from studies to influence research decisions across stakeholders throughout AISI and DSIT
There will be significant scope to contribute to the strategy of your workstream team and to design experiments with set-ups of increasing complexity.

Existing Civil Servants and applicants from accredited NDPBs are eligible to apply, but will only be considered on loan basis (Civil Servants) or secondment (accredited NDPBs). Prior agreement to be released on a loan basis must be obtained before commencing the application process. In the case of Civil Servants, the terms of the loan will be agreed between the home and host department and the Civil Servant. This includes grade on return.

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