Research Scientist/Engineer, Biological Safety

About the role

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.

What you’ll do

• Design, build, and run capability evaluations to assess what new models can do in the biological domain, and turn results into concrete deployment recommendations
• Develop training and evaluation datasets for our safety classifiers, working with internal and external threat modeling experts to ground them in realistic risk
• Train, tune, and iterate on safety classifiers with ML engineers, optimizing jointly for adversarial robustness and low false-positive rates
• Build the tooling and pipelines that make evaluation and classifier development fast and repeatable
• Analyze classifier and eval performance against production traffic, identify gaps, and prioritize improvements
• Design and run red-teaming and stress-testing of safeguards as threats, models, and product surfaces evolve
• Partner with Research, Product, and Policy teams to embed biological safety throughout the model development lifecycle
• Contribute to external communications including model cards, blog posts, and policy documents
• Track developments in biology, ML, and biosecurity for their potential to create new risks or enable new mitigations

What they’re looking for

Select a requirement to inspect fit, evidence or application context.

• Strong proficiency in Python, and extensive background in scientific programming and data analysis skills
• Excellent grasp of ML fundamentals and an ability to rapidly adopt ML development practices
• Excellent knowledge of modern biology across both measurement and engineering: high-throughput assays and functional characterization, as well as gene synthesis, genome editing, strain construction, and protein engineering
• Ability to build and maintain your own tooling rather than relying on others to implement your ideas
• Experience designing quantitative experiments or evaluations and drawing defensible conclusions from noisy results
• Strong analytical and writing skills, and the ability to explain technical concepts to non-technical stakeholders
• Familiarity with dual-use research concerns and biosecurity frameworks, such as select agent regulations, the Biological Weapons Convention, or Australia Group guidelines
• Comfort with ambiguity and with shifting priorities as AI capabilities change
• Can work independently while maintaining strong collaboration with cross-functional teams
• Are results-oriented, with a bias towards flexibility and impact
• Thrive in a fast-paced research environment where you balance rigorous scientific standards with rapid iteration
• A field relevant to the role as demonstrated through coursework, training, or professional experience

Helpful, not always essential

• Experience working with large language models, including prompting, fine-tuning, or evaluation
• Experience training or deploying classifiers or other ML systems in production
• Experience developing ML methods for biological systems or biological data
• Familiarity with adversarial robustness, red-teaming, or safety evaluation of ML systems
• Have at least 8 years of hands-on experience in life sciences, with deep expertise in areas such as molecular biology, drug discovery, or computational biology
• Experience leading complex technical projects across multiple stakeholder groups
• 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—$405,000 USD