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Research Engineer, AGI Safety and Alignment, DeepMind

Google
13 hours ago
Full-time
On-site
London, ENG

JobsCloseBy Editorial Insights

DeepMind’s AGI Safety and Alignment Team is seeking a Research Engineer to work onsite in London on a full-time basis. The role sits in ASAT and centers on reducing existential risk from AGI by researching alignment methods, building adversarially robust control systems, improving model interpretability, and helping teams apply research to products. Candidates should have a bachelor’s in CS or a related field, at least 3 years in software development or ML, and experience collaborating with research teams; preferred qualifications include applied safety research and experience training large models with RLHF. To apply, highlight concrete safety-focused projects, show collaboration with cross-functional teams, share links to code or papers, and tailor your narrative to ASAT’s mission.


Minimum qualifications:

  • Bachelor's degree in Computer Science, a related Software Engineering field, or equivalent practical experience.
  • 3 years of experience in software development, ML engineering, or ML research.
  • Experience working with research teams.

Preferred qualifications:

  • Experience conducting or contributing to applied research to improve the safety and alignment of frontier AI systems.
  • Experience with training large models (e.g., supervised finetuning, RLHF).

About the job

The Artificial General Intelligence (AGI) Safety and Alignment Team (ASAT) aims to reduce existential and catastrophic risk from AGI and eventually Artificial Superintelligence (ASI). We research novel techniques and work with the rest of GDM and Google to apply them. We advise executive leadership on safety.


ASAT has sub-teams specialising in making future Geminis more thoroughly aligned by finding and fixing sources of misalignment and exploring alignment techniques with better generalization. Preparing for future AGI risks by simulating them today and using interpretability techniques to understand AI and solve practical problems like model forensics or eval awareness. Building control for GDM’s agents as defense-in-depth against potential misaligned internal deployments. Researching training techniques, like debate, for aligning superhuman AI and ways to retain, improve, and measure monitorability. Researching and implementing ways to assess the ways in which a given model might be imperfectly aligned and developing and implementing tools and AI assistance that accelerates safety research. Advising executive leadership on risks posed by AI systems via the frontier safety framework based on our threat models and evaluations.

We are prioritising hires for deep alignment, alignment stress testing, language model interpretability, agent control, and amplified oversight. We are looking to grow our team with researchers and engineers. Depending on your background, we have opportunities available as both Research Scientists and Software Engineers.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Responsibilities

  • Research new alignment methods, studying alignment failures, and applying AGI-scalable alignment techniques to frontier models.
  • Develop adversarially robust AGI control systems and implement them in production.
  • Research interpretability techniques to understand what AI systems are ‘thinking’.
  • Work with product teams to ensure that our research is correctly adopted.