The AGI Safety and Alignment team at Google DeepMind is Hiring (July 2026)

Summary

GDM’s AGI Safety and Alignment Team is hiring for multiple roles, across all areas in this post on our recent work. This is the team at GDM, led by Rohin Shah, that aims to reduce existential risks from AI systems. You can listen to many of Rohin’s takes in his podcast on 80,000 hours. There is no one ‘type’ that we are looking for—we want excellent people. We think of the role as ‘member of technical staff’ though different people will have more of a research engineer or scientist flavour. We are flexible on location though most people will be most productive in either San Francisco or London. You should apply here (for the US) or here (for the UK) after reading the guidance here. Many of the basic facts about why ASAT is a good place to work and how we think about research are mostly unchanged since this post in 2025. What do we do? We are focused on risks of more severe harms from more advanced AI than the rest of GDM. You can read our high level AGI Safety and Security Approach. Our work includes aligning AGI, defending against misaligned deployments, and supporting coordinated safety. We’ve recently shared a recap of some of our recent work, which gives a better sense of what we do in practice. Deep alignment and stress testing are relatively new areas for us, created in response to increased capabilities, so in those areas there will likely be more flux in exactly what we do. We cover parts of our overall alignment approach and research directions in 5 minute talks in our AGI safety course. Unique aspects of ASAT If you’re reading this post, you probably already have a decent sense of the ways that technical safety teams at AI companies are different from other kinds of AGI safety work, so we won’t go into that. However, we think that ASAT also has a fairly different orientation towards AGI safety, relative to (our perception of) safety teams at Anthropic and OpenAI. Technical enablers for governance. Governance often has different technical requirements: for example, depending on the application, it may need to be more standardized, legible, applicable across AI companies, robust to gaming, etc. We try to anticipate governance and build the technical backing that it would need. [1] Examples include the first suite of dangerous capability evaluations, a metric that can identify latent reasoning architectures, and a method to measure AI progress over years (which powers the Epoch Capability Index). Prefer knowledge over advocacy. Everyone wants AI systems to be safe. Often, the challenges are factual disagreements (e.g. are software-only intelligence explosions plausible?) and execution (e.g. how do you align superhuman AI systems?). We view our role mainly as producing evidence to help resolve factual disagreements, and developing techniques to improve execution. As an example of how this attitude plays out, we studied the conditions under which training on CoT is risky or not, rather than producing demos where training against CoT is bad, or focusing on failures of CoT faithfulness. Focus on much more capable future systems [2] that would pose higher risks absent better mitigations. Our work tends to be driven more by conceptual arguments about future AI capabilities and risks, because we expect that much of the work on current AI systems won’t generalize to the systems that pose significant risks. Examples include MONA, debate, and honeypot evaluations. Coherent overall approach. ASAT aims for its work to feed into one overarching approach (which consists of many different bets). This is a major reason why we produce big technical roadmaps like An Approach to Technical AGI Safety and Security and the GDM AI Control Roadmap. To be clear, we’re not claiming that these are unalloyed goods. Advocacy is obviously important in some circumstances; it would be bad if everyone focused purely on knowledge. Our focus on future systems has likely led us to be late on impactful opportunities with present AI systems, such as work inspired by the persona selection model. Our main reason for writing this down is to select for candidates who are broadly enthusiastic about this approach, who will likely fit better in ASAT. Why should you join? First and foremost, you will be doing very impactful work as part of ASAT. We’re proud of the work we’ve done recently, and we believe we can accelerate similar work with more people. The unique aspects above also make our work somewhat more neglected in the research community; for much of the work discussed above, it seems plausible that no one would have done the work if we hadn’t done it. We have significant leadership support for our work. GDM and Google are pretty interested in being responsible and then setting norms and policy around safety. Our work both informs that process and also helps set the bar higher than it would otherwise be. Because Google has a reputation for seriousness and responsibility, actions Google takes generally have a greater