The role
From Google's own posting.
Google’s CBRNE team (Chemical, Biological, Radiological, Nuclear and Explosives) in Responsible Frontier AI Research makes sure that as Gemini gets better at science, it does not become a tool for catastrophic harm. We are a team of engineers working alongside biologists, chemists, and physicists. (Deep expertise in one field is plenty; nobody on the team covers all of them.) This is a research engineering role: you will do applied research, write production code, and launch the resulting safeguards to billions of users.
This role is probably not for you if you want to publish most of what you do, or if you are looking for a policy or purely research position. Expect strict data managing, tight release timelines, and an on-call rotation. You will read and debate technical material about mass-casualty harm and how to prevent it. You will dig into the small discrepancies that hint at big problems. One week you might design new safeguards; the next, find out why an evaluation result looked too good to be true, or attack our safeguards before someone else does.
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.
Build novel safeguards and attack them with automated and manual red teaming, while measuring their cost to legitimate scientific research.
Design, build and run evaluations for chemical, biological, radiological, nuclear and explosives risk in frontier models, including agentic systems risk.
Architect the reliability of key systems, including safeguards and incident response.
Partner with leading scientific experts to turn their judgment into datasets and automated graders, managing sensitive data under strict security controls.
Communicate results, including uncertainty and negative findings, to technical staff and leadership.
Minimum qualifications:
Bachelor degree or equivalent practical experience in a management, scientific, technical or engineering field.
8 years of experience writing code in a shared, version-controlled codebase with other contributors, which may include research software.
3 years of experience training, fine-tuning or evaluating machine learning models.
Preferred qualifications:
Master's degree or PhD in Chemistry, Physics, Chemical or Nuclear Engineering, Biology, or Computational Science, or a related field.
Experience building classifiers or evaluation infrastructure for large language models.
Experience applying statistics to machine learning evaluation.
Experience running software in production, including monitoring, on-call or incident response.
Experience in chemical, biological, radiological or nuclear security, export control, or adversarial work such as red teaming, security research or abuse prevention.
Bachelor degree or equivalent practical experience in a management, scientific, technical or engineering field.
8 years of experience writing code in a shared, version-controlled codebase with other contributors, which may include research software.
3 years of experience training, fine-tuning or evaluating machine learning models.