Research Scientist, Life Sciences

The shape of it

In the posting’s own words

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.

What it asks for · 9

• 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

Also a plus

• 5+ years of experience applying ML and software engineering to biological problems — computational biology, bioinformatics, protein ML, genomics, or similar
• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• experience15×
• biology13×
• bioinformatics11×
• pipelines10×
• product10×
• model8×
• research8×
• tools8×
• biological7×
• computational biology7×
• analysis6×
• build6×
• data6×
• discovery6×
• workflows6×
• biology bioinformatics5×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

More open at Anthropic

• Account Executive, AI NativeNew York City, NY; San Francisco, CA | New York City, NY
• Account Executive - DNBSingapore
• Account Executive, Public SectorSydney, Australia
• Account Executive - Public Sector (ASEAN)Singapore
• Account Executive, StartupsSan Francisco, CA | New York City, NY
• Accounting, Revenue Internal ControlsSan Francisco, CA | Seattle, WA