Research Scientist/Engineer, 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

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 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

Preferred qualifications

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

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 pe