If our work sounds interesting, we encourage you to apply. All applications are assessed on a rolling basis, so it is best to apply as soon as possible.
"AISI has built a wonderful group of technical, policy, and civil service experts focused on making AI and AGI go well, and I'm finding it extremely motivating to do technical work in this interdisciplinary context."
Geoffrey Irving
Chief Scientist
"AISI is situated at the centre of the action where true impact can be made. I'm excited about the opportunities unfolding in front of us at such a rapid pace."
Professor Yarin Gal
Research Director
Our typical interview process includes submitting a CV and short written statement, skills assessments such as a technical interview and a take-home coding test, and 2-4 interviews, including a conversation with a senior member of our team. We tailor this process as needed for each role and candidate.
Please note that if you're applying to our technical roles, this privacy policy applies.
This application is for those without a preference for a team. We prefer you apply to team-specific RE roles below. /// Design and build evaluations to assess the capabilities and safety of advanced AI systems. Candidates should have relevant experience in machine learning.
This application is for those without a preference for a team. We prefer that you applying team-specific RS roles below. /// Lead research projects to improve our ability to assess the capabilities and safety of advanced AI systems. Candidates should have relevant experience in machine learning.
Work with one of our evaluations workstreams or our cross-cutting platforms team to build interfaces and frontends, create fast and secure inference channels for external models, and drive ML ops projects to host and fine-tune our own models. Candidates should have experience with some of the following: frontend or tools development, backend/API design, dev/ML ops, privacy and security engineering, and/or engineering management.
Design experiments and build evaluations to assess the cyber offensive capabilities of advanced AI systems. Candidates should have relevant experience in machine learning and cybersecurity.
Drive projects to understand advanced AI systems' vulnerability to misuse. Candidates should bring experience in ML research, ML engineering, or in security (e.g. redteaming in other domains).
Lead research projects to improve our ability to assess the cyber offensive capabilities of advanced AI systems. Candidates should have relevant experience in machine learning and cybersecurity.
As a team manager, you'll be heading up a multi-disciplinary team including scientists, engineers and domain experts on the capabilities that we are evaluations. These include autonomous replication, AI R&D, manipulation and deception.
Build large-scale experiments, to empirically evaluate risks such as uncontrolled self-improvement, autonomous replication, manipulation and deception. Collaborate with others to push forward the state of the science on model evaluations.
Research risks such as uncontrolled self-improvement, autonomous replication, manipulation and deception. Improve the science of model evaluations with things like scaling laws for dangerous capabilities.
Advance our understanding of AI risk scenarios involving AI research & development, autonomous replication, deception, and resource acquisition. Candidates should have a deep understanding of machine learning.
As an interpretability research scientist or engineer, you'll lead early work to push forward the science on detecting scheming and white-box evaluations.
As a Strategy and Delivery Adviser, you will be working with a team of research scientists and engineers to drive forward cutting-edge research at the intersection of advanced AI and biological/chemical science. This is a multi-faceted role which involves a mixture of strategy, policy and project management.
Drive research to develop our understanding of how safety cases could be developed for advanced AI. You'll work closely with Geoffrey Irving to build out safety cases as a new pillar of AISI's work.
As a Strategy and Delivery Adviser, you will be working with a team of research scientists and engineers to drive forward cutting-edge AI safety research on the highest priority issues. You’ll provide crucial support for a team working on a specific set of AI safety issues E.g cyber risks, chem-bio risks, safety cases etc.
You will be a part of the Testing Team, which is responsible for our overall testing strategy, and the end-to-end preparation and delivery of individual testing exercises. You will collaborate closely with researchers and engineers from our evaluations workstreams, as well as policy and delivery teams. Your role will be broad and cross-cutting, involving project management, strategy, and scientific and policy communication.
As workstream lead of a novel team, you will build a team to evaluate and mitigate some of the pressing societal-level risks that Frontier AI systems may exacerbate, including radicalization, misinformation, fraud, and social engineering.
As workstream lead for this novel team, you will build and lead a multidisciplinary team to evaluate and mitigate the behavioural and psychological risks that emerge from AI systems. Your teams’ work will address how human interaction with advanced AI can impact human users, with a focus on identifying and preventing negative outcomes.
AISI is expanding our Systemic Safety team. This team is focussed on identifying and catalyzing interventions which could advance the field of AI safety and strengthen the systems and infrastructure in which AI systems are deployed. As the Workstream Lead for this team, you will build and lead a multidisciplinary team focussed on pushing systemic safety forward as an agenda and creating the global environment for responsible innovation.