Applied AI Engineer for Life Sciences Deployments

Anthropic; New York (NY); Hybrid; USD 280,000 - 320,000; Full time

Benefits offered by this job

Equity donation matching

Generous vacation

Parental leave

Flexible working hours

Office space

Job summary

Anthropic is seeking an Applied AI Engineer to join Beneficial Deployments, focusing on maximizing Claude's impact in life sciences. You'll collaborate with prestigious research partners, prototype agents, and build ecosystem tooling to support science from discovery to translation.

This role blends research and practical software engineering, demands deep domain knowledge in life sciences, and requires shipping production code while navigating cross-disciplinary teams.

Qualifications

• Deep research experience in life sciences, biomedical research, or scientific computing.
• Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks.
• Builder credibility from shipping production code as a software engineer, forward-deployed engineer, or technical founder.

Responsibilities

• Partner with flagship life sciences institutions to integrate projects into production systems.
• Develop reusable ecosystem infrastructure, e.g., MCP servers, instruments, benchmarks, and agent skills.
• Identify hard deployment challenges in life sciences and relay findings to product/engineering/research teams.
• Create technical content and documentation to enable partners to self-serve.

Skills

Life sciences research

LLM-powered tools

Production code shipping

Ambiguity handling

Education

Bachelor's degree

Tools

MCP servers

Agent architectures

Job description

Anthropic is seeking an Applied AI Engineer to join Beneficial Deployments, focusing on maximizing Claude's impact in life sciences. You'll collaborate with prestigious research partners, prototype agents, and build ecosystem tooling to support science from discovery to translation.

This role blends research and practical software engineering, demands deep domain knowledge in life sciences, and requires shipping production code while navigating cross-disciplinary teams.