We are looking for biological scientists to help build safety and oversight mechanisms for our AI systems. As a Safeguards Biological Safety Research Scientist, you will apply your technical skills to design and develop our safety systems which detect harmful behaviors and to prevent misuse by sophisticated threat actors. You will be at the forefront of defining what responsible AI safety looks like in the biological domain, working across research, policy, and engineering to translate complex biosecurity concepts into concrete technical safeguards. This is a unique opportunity to shape how frontier AI models handle dual-use biological knowledge—balancing the tremendous potential of AI to accelerate legitimate life sciences research while preventing misuse by sophisticated threat actors.
Biological Safety Research Scientist at Anthropic
Remote - San Francisco, CA
More jobs at AnthropicSalary
USD 300,000 - 405,000
Requirements
Skills
- PhD in molecular biology, virology, microbiology, biochemistry, systems or computational biology, or related life sciences field, or equivalent professional experience
- Extensive experience in scientific computing and data analysis, with proficiency in programming (Python preferred)
- Deep expertise in modern biology, including both reading (high-throughput measurement, functional assays) and writing (gene synthesis, genome editing, strain construction, protein engineering) techniques
- Familiarity with dual-use research concerns, select agent regulations, and biosecurity frameworks
- Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders
- Passion for learning new skills and the ability to rapidly adapt to changing techniques and technologies
- Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve
Responsibilities
- Design and execute capability evaluations (evals) to assess the capabilities of new models
- Collaborate closely with internal and external threat modeling experts to develop training data for our safety systems, and with ML engineers to train these safety systems, optimizing for both robustness against adversarial attacks and low false-positive rates for legitimate researchers
- Analyze safety system performance in traffic, identifying gaps and proposing improvements
- Develop rigorous stress-testing of our safeguards against evolving threats and product surfaces
- Partner with Research, Product, and Policy teams to ensure biological safety is embedded throughout the model development lifecycle
- Contribute to external communications, including model cards, blog posts, and policy documents related to biological safety
- Monitor emerging technologies for their potential to contribute to new risks and new mitigation strategies, and strategically address these
Technologies
PythonLarge Language ModelsMachine LearningScientific ComputingData Analysis
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