About this role
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 research engineers to build the safety and oversight mechanisms that govern how our models handle biological knowledge. As a bio safety researcher, you will spend your time: designing and running capability evaluations against frontier models, generating and curating training data for our safety classifiers, training and iterating on those classifiers alongside our ML engineers, and measuring how they hold up against adversarial pressure in production traffic.
This work sits at the intersection of applied ML and biosecurity. You will help define what responsible AI safety looks like in the biological domain, translating threat models into evals, datasets, and deployed systems. The core technical tension you will own is precision: safeguards need to be robust against sophisticated actors while staying out of the way of the far larger population of legitimate researchers using Claude to accelerate life sciences work. Getting that tradeoff right is an empirical problem, and you will be the person measuring it.
You do not need to be an ML researcher today. We are looking for strong scientific programmers with real depth in modern biology who want to bring that depth to bear on model evaluation and classifier development.
Key responsibilities
• 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
Minimum qualifications
• 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 p