Research Manager, Biological Safety

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.

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

• 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.

$405,000—$485,000 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 you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.