ML Research Scientist - Safety Cases

We will review applications on an ongoing basis.

As a Machine Learning Research Scientist working on safety cases, you will conduct foundational research to help take this ambitious new pillar of AISI’s work forward. By building our understanding of how AI safety cases could be developed, you will help to expand AISI’s programme of technical work beyond the existing workstreams focussed on evaluating model capabilities and safeguards. 

Safety cases are already used as standard in other industries and are structured arguments that a system is unlikely to cause significant harm if deployed in a particular setting. As the AI frontier develops, we expect safety cases could become an important tool for mitigating AI safety risks, whereby AI companies set out detailed arguments for how they have ensured their models are safe. We believe it is possible to significantly develop our understanding of what a good safety case would look like now, even though the field is far from knowing how to write a detailed safety case.

In this role, you would help push this understanding forward, both by direct technical research and via technical collaborations with external researchers and organisations. Key areas to cover include open problems that must be overcome to increase our confidence within specific safety agendas, and agenda-specific evaluations such as control and alignment evaluations. 

The role offers a unique opportunity to work closely alongside the world’s best technical talent, including Chief Scientist Geoffrey Irving who leads the safety case workstream, as well as talented Policy / Strategy leads and other Research Engineers and Research Scientists.  You will also collaborate with external topic-level experts, partner organisations and policy makers to coordinate and build on external research. There will be significant scope to contribute to the overall vision and strategy of the safety case team as an early hire. 

We view work on safety cases at AISI as a critical component of the overall safety story, alongside our existing workstreams focused on evaluations of dangerous capabilities and safeguard effectiveness. 

RESPONSIBILITIES 

This role offers the opportunity to progress deep technical work at the frontier of AI safety and governance. Your work would likely include: 

• Detailed research on safety cases.  We think it’s important to get into the details, so this might mean trying to write a comprehensive safety case based on evals for a certain hazard, or developing novel methods for AI control or formal verification.
• High-level research into safety case material (e.g. what methods might be used, and what properties one would need for these arguments to be correct).
• Input into our strategy, which focuses on: how to get more safety case work to occur, what form they should take, and how to improve the chance that the results improve safety.
• Collaboration with external partners (e.g. labs, academics) on joint research into safety cases.
• Research organisational work (assembling teams, organising workshops) to create an environment where safety case work occurs.

You may be a good fit if you have some of the following skills, experience and attitudes:

• Relevant machine learning research experience in industry, relevant open-source collectives, or academia in a field related to machine learning, AI, AI security, or computer security.
• Broad knowledge of technical safety methods (T-shaped: some deep knowledge, lots of shallow knowledge).
• Strong writing ability.
• Motivated to conduct technical research with an emphasis on direct policy impact rather than exploring novel ideas.
• Ability to work autonomously and in a self-directed way with high agency, thriving in a constantly changing environment and a steadily growing team, while figuring out the best and most efficient ways to solve a particular problem.
• Bring your own voice and experience but also an eagerness to support your colleagues together with a willingness to do whatever is necessary for the team’s success and find new ways of getting things done within government.
• Have a sense of mission, urgency, and responsibility for success, demonstrating problem-solving abilities and preparedness to acquire any missing knowledge necessary to get the job done.
• Comprehensive understanding of large language models (e.g. GPT-4). This includes both a broad understanding of the literature, as well as hands-on experience with things like pre-training or fine tuning LLMs. 
• Direct research experience (e.g. PhD in a technical field and/or spotlight papers at NeurIPS/ICML/ICLR).  
• Experience working with world-class multi-disciplinary teams, including both scientists and engineers (e.g. in a top-3 lab).  

Salary & Benefits

We are hiring individuals at all ranges of seniority and experience within the research unit, and this advert allows you to apply for any of the roles within this range. We will discuss and calibrate with you as part of the process. The full range of salaries available is as follows: