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Chemist, DeepMind

Google
New York City, USA
Junior · 2+ years experience
Onsite
USD 174000-252000 / year
Discovered 2 weeks ago
python

Full Job Posting

Responsibilities

  • 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.

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