Biological Safety Research Scientist

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role 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. In this role, you will: - 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 You may be a good fit if you have - A PhD in molecular biology, virology, microbiology, biochemistry, systems or computational biology, or a 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" (e.g. high-throughput measurement, functional assays) and "writing" (gene synthesis, genome editing, strain construction, protein engineering) techniques in biology - Familiarity with dual-use research concerns, select agent regulations, and biosecurity frameworks (e.g., Biological Weapons Convention, Australia Group guidelines) - Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders - Have a passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies - Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve Preferred Qualifications - Background in AI/ML systems, particularly experience with large language models - Experience in developing ML for biological systems - Extensive experience in complex projects with multiple stakeholders The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $300,000—$320,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge

A PhD in molecular biology, virology, microbiology, biochemistry, systems or computational biology, or a 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 in biology. Familiarity with dual-use research concerns, select agent regulations, and biosecurity frameworks (e.g., Biological Weapons Convention, Australia Group guidelines). Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders. Have a passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies. Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve.

Design and execute capability evaluations ("evals") to assess the capabilities of new models. Collaborate with internal and external threat modeling experts to develop training data for safety systems, and with ML engineers to train these safety systems, optimizing for 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 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.