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. 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. 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.

Requirements

Must-haves

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
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
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

Nice-to-haves

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
Experience leading complex technical projects across multiple stakeholder groups
The annual compensation range for this role is listed below.
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.
How we're different
Come work with us!

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Benefits

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Parental Leave