Opening. This role is for a Research Engineer focusing on Chemical, Biological, Radiological and Nuclear (CBRN) AI Responsibility at Google DeepMind. The role involves developing, implementing, and maintaining evaluations and mitigations, and the infrastructure that supports them.
What you'll do
As we develop models with strong capabilities in the physical sciences, we are looking for Research Engineers to make sure these capabilities are always put to good use.
Using your knowledge of machine learning and science, you will help design new evaluations for CBRN risk, communicate the results clearly, and ensure the safety of our AI systems. You will work closely with other Engineers and Research Scientists as well as with experts in AI ethics and policy.
Key responsibilities
- Design and develop robust evaluations to test potential CBRN risks arising from cutting-edge AI models with strong capabilities in the physical sciences.
- Develop and maintain infrastructure for these evaluations.
- Run these evaluations prior to releases for new AI models.
- Resolve potential CBRN risks with state-of-the-art mitigations.
- Clearly communicate results to decision-makers.
- Collaborate with subject matter experts in CBRN, AI policy and ethics, model development, and scientific research.
- Develop and maintain an understanding of trends in AI development, CBRN threat landscapes, governance, and relevant sociotechnical research to inform the design of new evaluations.
What you need
In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:
- Bachelor's degree in a technical subject (e.g., computer science, engineering, machine learning, mathematics, physics, statistics) or a relevant scientific field with strong computational experience (e.g., bioinformatics, computational chemistry, biotechnology, computational materials science), or equivalent practical experience.
- Proven ability to write clean, maintainable, and efficient Python.
- Knowledge of concepts in mathematics, statistics, and machine learning needed for understanding cutting-edge research in AI, CBRN-related modeling, and risk assessment.
- Ability to present and explain technical results clearly to non-experts and leadership stakeholders.
