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Computational Biologist, DeepMind

Google
New York City, USA
Mid
Onsite
USD 174000-252000 / year
Discovered 2 weeks ago

Full Job Posting

Responsibilities

  • Develop assessments and frameworks to rigorously quantify capability thresholds, technical feasibility, and potential consequences of model uplift wherever possible.
  • Design and execute robust evaluations and relevant datasets of computational biology and/or microbiology and other critical related areas. The goal of these evaluations is to elicit the maximum capability of the model to determine how it can uplift malicious actors across the domain.
  • Partner with responsibility engineers to design highly nuanced mitigations that block harmful outputs without overly degrading the model's scientific utility. Similar to writing capability evaluations, this requires a deep understanding of dual use science.
  • Support the ongoing development and refinement of Google’s CBRNE safety policy, and clearly communicate complex risks, evaluation findings, and mitigation strategies to leadership and technical and non-technical external stakeholders.

Minimum qualifications:

  • PhD in Computational Biology, Bioinformatics, Biochemistry, Microbiology, or a related field or equivalent practical experience.
  • Experience applying computational tools, biological modeling, and command-line scripts to analyze protein biochemistry, microbiology, or pathogenic systems.
  • Experience working with biological datasets (e.g., genomics, proteomics, structural biology).
  • Experience evaluating emerging technologies and assessing potential biorisk or misuse scenarios.
  • Experience with peer-reviewed contributions and working across cross-functional teams.
  • Experience supporting a secure, global research environment.

Preferred qualifications:

  • Experience working in AI safety, evaluations, red-teaming, or applied AI research at a national laboratory, academic institution, or industry setting.
  • Knowledge of challenges of biological weapons, the biological threat landscape, potential mitigations and awareness of relevant stakeholders.
  • Familiarity with agentic AI and its applicability to frontier risk within biology.
  • Understanding of Safety Frameworks in AI. Knowledge of the intersection of microbiology/virology within AI safety.
  • Intuition and creativity for how frontier AI models or agents could enhance these capabilities and lower barriers for bad actors.
  • Active security clearance, or the eligibility to obtain and maintain one.

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