Design, execute, and critically review the outputs of computational chemical evaluations, including automated synthesis planning, predictive toxicology, and molecular docking, to test the maximum agentic capabilities of our AI models. Review model outputs of synthetic chemistry evaluations, commenting on key metrics (such as accuracy) and the potential uplift provided by the model in the context of a given threat actor. Provide recommendations for implementing mitigations to ensure the chemical safety of our models, while minimizing the impact on legitimate science. Use information provided from evaluations and other data sources to update harm frameworks and threat models. Communicate technical results and commentary to audiences with a range of expertise, from fellow chemists to executive decision-makers.
Minimum qualifications:
PhD in synthetic or computational chemistry, or equivalent practical experience.2 years of post-doctoral, industry, or defense experience, including wet-lab bench experience designing, executing, and troubleshooting multi-step syntheses. Experience in cheminformatics and digital drug discovery tooling, specifically using Python (e.g., RDKit) to analyze reaction and bioactivity/toxicity datasets (e.g., QSAR), alongside ML (e.g., protein-ligand co-folding) or physics-based (e.g., FEP) binding affinity prediction. Experience with computer-aided synthesis planning (CASP), retrosynthesis tools, and 3D structure-based modeling, such as molecular docking and pharmacophore modeling. Experience working with Scientific AIs and applying LLMs to scientific problems.
Preferred qualifications:
Experience with dual-use chemistry, the Chemical Weapons Convention (CWC) and Schedules, and other national and international chemical control treaties and documents.
Understanding (without deep technical detail) of the training and operating principles of LLMs.
Understanding (without deep technical detail) of the mitigation strategies applied to LLMs to prevent the proliferation of dual-use scientific information.
Understanding of model evaluation methodologies for determining the frontier risks of modern AI models; see Google's FSF and similar documents at other AI labs.
Google's CBRNE team (Chemical, Biological, Radiological, Nuclear and Explosives) makes sure that as Gemini gets better at science, it does not become a tool for catastrophic harm. We would like to invite applications from qualified chemists for the position of a Chemist Subject Matter Expert (SME) in the CBRNE team within the Responsible Frontier AI Research (RFAIR) team, joining SMEs with broad experience across Chemistry.
The successful applicant will be hired for a Research Scientist position serving as a technical expert responsible for evaluating and mitigating safety risks of frontier AI models (primarily but not limited to LLMs) in the chemistry domain.
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Note:
By applying to this position you will have an opportunity to share your preferred working location from the following: New York, NY, USA; Mountain View, CA, USA.PhD in synthetic or computational chemistry, or equivalent practical experience.2 years of post-doctoral, industry, or defense experience, including wet-lab bench experience designing, executing, and troubleshooting multi-step syntheses. Experience in cheminformatics and digital drug discovery tooling, specifically using Python (e.g., RDKit) to analyze reaction and bioactivity/toxicity datasets (e.g., QSAR), alongside ML (e.g., protein-ligand co-folding) or physics-based (e.g., FEP) binding affinity prediction. Experience with computer-aided synthesis planning (CASP), retrosynthesis tools, and 3D structure-based modeling, such as molecular docking and pharmacophore modeling. Experience working with Scientific AIs and applying LLMs to scientific problems.
