Research Manager, Biological Safety

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

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, and classifiers that govern how our models handle biological knowledge.

You will lead a team of research scientists and engineers who design and run capability evaluations against frontier models, curate training data for our safety classifiers, train and iterate on those classifiers alongside our ML engineers, and measure how they hold up against adversarial pressure in production traffic. You will set the technical direction for that work, decide where the team invests, and own the results.

This is a hands-on management role. Most of your time goes to growing and directing the team, but you will keep enough technical depth to review an eval design, interrogate a classifier's failure modes, and represent the work credibly to Research, Product, and Policy partners.

The core tension your team owns 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 your team is the one measuring it.

Key responsibilities

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

Establish how the team measures classifier and eval performance against production traffic, identifies gaps, and prioritizes improvements

Direct red-teaming and stress-testing of safeguards as threats, models, and product surfaces evolve

Partner with Research, Product, Policy, and government affairs colleagues to embed biological safety throughout the model development lifecycle, and serve as an escalation point for biological content

Represent the team's work in external communications including model cards, blog posts, and policy documents

Track developments in biology, machine learning, and biosecurity for their potential to create new risks or enable new mitigations

Minimum qualifications

Experience managing a technical team, including hiring, coaching, and performance management

A record of setting technical direction for a team and making prioritization calls under uncertainty

Proficiency in Python, with a background in scientific programming and data analysis

A solid grasp of ML fundamentals, sufficient to critically review evaluation design and classifier development

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

Experience designing quantitative experiments or evaluations and drawing defensible conclusions from noisy results

Clear 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

Motivation to prevent misuse without obstructing the beneficial work that makes up the vast majority of this field

Preferred qualifications

3+ 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

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:

$405,000 — $485,0