AI Safety Computational Biologist – Frontier Risk

Google; New York (NY); Hybrid; USD 174,000 - 252,000; Full time

Benefits offered by this job

Equity

Bonus

Benefits

Job summary

Google DeepMind is seeking a Computational Biologist SME to evaluate and mitigate safety risks of frontier AI models in the biology domain. The role focuses on model assessment, dataset design, and collaboration with responsibility engineers to create nuanced mitigations while preserving scientific utility.

The position spans work across the CBRNE team within the Responsible Frontier AI Research group, with emphasis on safe model uplift and dual-use considerations.

Qualifications

• PhD or equivalent practical experience in computational biology or related field.
• Experience applying computational tools to analyze protein biochemistry, microbiology, or pathogenic systems.
• Experience with biological datasets (genomics, proteomics, structural biology).
• Experience evaluating emerging technologies and assessing biorisk or misuse scenarios.
• Experience with peer-reviewed contributions and cross-functional teams.
• Experience supporting a secure, global research environment.

Responsibilities

• Develop assessments to quantify capability thresholds and potential consequences of model uplift.
• Design and execute evaluations and datasets in computational biology/microbiology.
• Partner with responsibility engineers to design mitigations that balance safety and scientific utility.
• Support development and communication of Google’s CBRNE safety policy to leadership and stakeholders.

Skills

Computational biology

Biology modeling

Data analysis

Command-line scripting

Cross-functional collaboration

Security mindset

Education

PhD in Computational Biology / Bioinformatics or related field

Job description

Google DeepMind is seeking a Computational Biologist SME to evaluate and mitigate safety risks of frontier AI models in the biology domain. The role focuses on model assessment, dataset design, and collaboration with responsibility engineers to create nuanced mitigations while preserving scientific utility.

The position spans work across the CBRNE team within the Responsible Frontier AI Research group, with emphasis on safe model uplift and dual-use considerations.