Drimva

Gray Swan AI

Head of Biology

$230K–$280K

Remote (U.S. Only) · Remote · Director · Full-time · Posted Sep 3, 2026

Experience
Postdoctoral or equivalent research experience; no number of years specified.
Education
PhD in biology or a closely related discipline.
Visa sponsorship
Visa sponsorship available for exceptional candidates

About the job

Leads Gray Swan AI’s biology safety capability, evaluating biological misuse risks in frontier AI and building scalable safety systems, standards, and a specialist team.

What you'll do

  • Lead adversarial evaluations of frontier AI systems for biological misuse risks across text, agentic, and multimodal capabilities.
  • Work with AI researchers and engineers to develop evaluation datasets, classifiers, guardrails, benchmarks, and automated red-teaming; maintain biological risk frameworks and advise stakeholders.
  • Build and lead a biology safety team, establish evaluation standards and infrastructure, and represent the company with external partners.

What they ask for

  • PhD in biology or a closely related discipline, with postdoctoral or equivalent research experience.
  • Deep understanding of biological misuse risks, dual-use research, pathogen biology, synthetic biology, or biosecurity.
  • Experience in adversarial evaluations, biological risk assessment, AI safety research, or safety datasets for frontier AI systems.
  • Familiarity with international biosecurity frameworks, dual-use governance, responsible biotechnology practices, and emerging AI safety standards.

Nice to have

  • Experience developing machine learning systems, biological safety classifiers, or evaluation pipelines for high-risk biological capabilities.
  • Frontier AI evaluations, red-teaming, prompt engineering, AI benchmarking, or programming/data science experience (including Python).

Pay

$230,000 - $280,000 USD base salary; $230K – $280K USD base salary

Tools

Python

AI focus

Evaluates frontier AI for biological misuse risks and develops AI safety evaluations, datasets, classifiers, guardrails, benchmarks, fine-tuned models, and automated red-teaming.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 6, 2026 (UTC)