Want to know if this job is worth applying to?
San Francisco, California, United States
Hybrid
Available
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.
We're seeking an exceptional Research Scientist to join our Life Sciences team at Anthropic. Our team is building a world-class research group focused on making Claude a superhuman life sciences research assistant. This role sits at the intersection of machine learning, software engineering, and biology — you'll directly improve model capabilities on scientific tasks through post-training, evaluation design, and RL environment development.
As a core member of our Life Sciences team, you'll work in a high-impact team that translates deep biological domain knowledge into model training objectives, benchmarks, and agentic workflows. You'll help establish Anthropic as a leader in AI-accelerated biology while shaping how frontier models reason about and execute computational biology tasks.
This role offers a unique opportunity to shape how frontier AI models learn to do biology. You'll work alongside some of the world's best AI researchers while tackling problems that matter for human health and scientific understanding. If you're excited about turning your computational biology expertise into model capabilities, we want to hear from you.
Build and ship agentic tools and integrations that let Claude execute real life science workflows — bioinformatics pipelines, database queries, analysis notebooks, literature review
Design and build evaluation benchmarks that measure model capabilities on biology tasks — figure interpretation, bioinformatics, protocol reasoning, literature synthesis
Work closely with product and design teams to scope, prototype, and ship features for life sciences users
Partner with external biotech, pharma, and academic users to understand their workflows and turn feedback into product improvements
Build and maintain the engineering infrastructure behind our biology product surface — tool scaffolding, data pipelines, eval harnesses
Translate biological domain knowledge into product requirements and evaluation criteria that guide model improvement
Experience applying ML and software engineering to biological problems — computational biology, bioinformatics, protein ML, genomics, or similar
Experience working in drug discovery or development at a biotech or pharma company, or conducted fundamental research in an academic setting — with an understanding of what real scientific workflows look like and where they break down
Strong software engineering skills: comfortable building production-quality Python, working in large codebases, and owning infrastructure end-to-end
Hands-on experience training or fine-tuning ML models (LLMs, protein language models, or other deep learning architectures)
A track record of shipping computational tools or pipelines that biologists actually use
Comfortable navigating ambiguity and defining problems in a rapidly evolving research environment
Able to work independently while collaborating tightly with research, product, and domain-expert teams
Results-oriented with a bias toward rapid iteration and measurable impact
Passionate about using AI to accelerate scientific discovery while maintaining high ethical standards
Ph.D. in computational biology, bioinformatics, bioengineering, CS, or a related quantitative field — or equivalent industry experience
Experience with LLM post-training: RLHF, RL from verifiable rewards, SFT data curation, or eval-driven development
Direct experience with therapeutic discovery pipelines — target identification, lead optimization, ADMET modeling, or clinical data analysis
Familiarity with bioinformatics tooling and pipelines (sequence analysis, structure prediction, single-cell, variant calling, etc.)
Experience building agentic systems or tool-use environments
Published research in ML for biology, or open-source contributions to computational biology tools
Fluency with biological databases (UniProt, PDB, Ensembl, NCBI) and the ability to reason about their schemas and failure modes
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.
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.
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.
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.