Research Scientist/Engineer, Biological Safety

Anthropic; San Francisco (CA); Hybrid; USD 300,000 - 405,000; Full time

Benefits offered by this job

Competitive compensation

Equity donation matching (optional)

Generous vacation and parental leave

Flexible working hours

Office space in San Francisco

Job summary

Anthropic in San Francisco seeks a bio safety researcher to design and run capability evaluations for biology-focused models, build and curate datasets for safety classifiers, and iterate on those classifiers with ML engineers.

You will work at the intersection of applied ML and biosecurity, translating threat models into evals, datasets, and deployed systems, with emphasis on precision and robust safeguards.

Qualifications

• Strong proficiency in Python and scientific programming
• Solid understanding of ML fundamentals and development practices
• Extensive biology knowledge including assays and genome editing
• Ability to build and maintain your own tooling
• Experience designing quantitative experiments or evaluations
• Strong analytical and writing skills, able to explain concepts to non-technical stakeholders

Responsibilities

• Design, build, and run capability evaluations for biology models and turn results into deployment recommendations
• Develop training and evaluation datasets for safety classifiers with threat-model grounding
• Train and iterate on safety classifiers with ML engineers for robustness and low false positives
• Build tooling and pipelines to speed up evaluation and classifier development
• Analyze classifier and eval performance against production traffic and identify gaps
• Lead red-teaming and stress-testing of safeguards as models evolve
• Collaborate with Research, Product, and Policy teams to embed biology safety throughout development
• Contribute to external communications including model cards, blogs, and policy documents
• Track developments in biology, ML, and biosecurity for new risks or mitigations

Skills

Python

ML fundamentals

Biology knowledge

Experiment design

Communication

Independent working

Education

Bachelor's degree

Job description

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.

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.

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.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.