The opportunity
Anthropic's Safeguards organization builds the policies, evaluations, and enforcement systems that keep our models from contributing to catastrophic harm. We are hiring a manager to lead the research engineering team responsible for biological safety: the evaluations, datasets,…
What you'll do
Manage, coach, and grow a team of research scientists and engineers working: on biological safety evaluations and classifiers, including hiring, onboarding, performance, and career development
Set the technical direction and roadmap for the biological safety research: agenda, and make the calls about what the team builds, what it deprioritizes, and when a safeguard is ready to ship
Own the quality of capability evaluations that assess what new models can do: in the biological domain, and turn results into deployment recommendations that leadership can act on
Guide the development of training and evaluation datasets for our safety: classifiers, working with internal and external threat modeling experts to ground them in realistic risk
Oversee the training and iteration of safety classifiers alongside ML: engineers, optimizing jointly for adversarial robustness and low false-positive rates
Ensure the team invests in the tooling and pipelines that make evaluation and: classifier development fast and repeatable
What they're looking for
- + years of people management experience, ideally leading research scientists,: research engineers, or ML engineers
- Experience building a team or function from a small headcount, including: defining scope, hiring the first few people, and establishing how the team works
- 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 working with large language models, including prompting, fine-tuning, or evaluation
- Experience training or deploying classifiers or other ML systems in: production, and comfort reasoning about precision and recall for rare, high-consequence categories where the base rate is very low
- Experience developing ML methods for biological systems or biological data
- Familiarity with adversarial robustness, red-teaming, or safety evaluation of ML systems
- Experience leading complex technical projects across multiple stakeholder groups
