Principal AI Scientist, Agentic LLMs for Life Sciences

Arlyn AI; New York (NY); On-site; USD 175,000 - 255,000; Full time

Benefits offered by this job

Equity grants

Unlimited PTO

Employee benefits package

Job summary

Arlyn AI, a New York-based startup focused on AI-powered insights for life sciences, seeks a Principal/Senior Data Scientist for Agentic AI to transform unstructured data into actionable ontologies and drug-access insights.

You will own end-to-end LLM applications, design production-ready AI systems, and collaborate with Engineering and Data Science to scale infrastructure and improve performance, with on-site work at Hudson Yards, NYC.

Qualifications

• 5+ years of industry experience in finance, biotech, or tech startups, or a PhD in a quantitative field with 2+ years of industry experience.
• Hands-on experience building AI applications, including prompt engineering, advanced analytics, and transforming large-scale unstructured data into production-ready solutions.
• Ability to translate complex, ambiguous business challenges into clear, structured data problems and drive decision-making.

Responsibilities

• Own the end-to-end development and deployment of agentic LLM applications that deliver measurable business impact.
• Design, build, evaluate, and ship production-ready AI systems from prototype to production.
• Partner with founders to define product direction and turn business problems into AI solutions.
• Collaborate with Engineering to build scalable AI infrastructure, system architecture, and internal tools.
• Improve system performance, reliability, and user experience through rapid experimentation and iteration.

Job description

Arlyn AI, a New York-based startup focused on AI-powered insights for life sciences, seeks a Principal/Senior Data Scientist for Agentic AI to transform unstructured data into actionable ontologies and drug-access insights.

You will own end-to-end LLM applications, design production-ready AI systems, and collaborate with Engineering and Data Science to scale infrastructure and improve performance, with on-site work at Hudson Yards, NYC.