Applied AI Engineer, Beneficial Deployments (Life Sciences)

Anthropic · San Francisco, CA | New York City, NY · Applied AI · listed August 24, 2026

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The shape of it

Seniority

Not stated

Where

Hybrid

Stated pay

$280,000 – $320,000 USD

Requirements listed

Length

1,055 words

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 · 2

Deep research experience in life sciences, biomedical research, or scientific computing. Bonus if you've studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics.
Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks.

What the job covers

Partner deeply with flagship life sciences research institutions — understand their scientific workflows end-to-end, build hands-on with their engineering teams, and help take projects from early exploration to production systems integrated into how they do science day-to-day.
Develop reusable ecosystem infrastructure, like MCP servers for domain-specific data sources (genomics platforms, literature databases, experimental repositories), instruments, scientifically-grounded benchmarks, and agent skills that other institutions can adopt without starting from scratch.
Identify what's actually hard about deploying AI in life sciences (heterogeneous data, auditability requirements, the prototype-to-trust gap) and feed those findings back to product, engineering, and research.
Create technical content and documentation that lets partners self-serve, so what works for one institution can scale globally without the same level of hand-holding.

Tools and skills named

Models & research

LLM
Prompt engineering

Ways of working

Technical writing

Words the posting leans on

research4×
engineering3×
institutions3×
life sciences3×
technical3×
agent2×
building2×
comfortable2×
data2×
deeply2×
engineer2×
experience2×
genomics2×
partner2×
production2×
scientific2×

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.

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