Agentic AI Engineer for Scientific Discovery – Hybrid

CyRAD Talent Solutions; New York (NY); Hybrid; USD 250,000 - 600,000; Full time

Benefits offered by this job

Sign-on bonus

Year-end bonuses

Hybrid work schedule

Office in NYC

Job summary

Agentic AI Initiative is building a new AI program in New York, blending agentic systems, generative AI, scientific computing, and drug discovery. This software-first role emphasizes end-to-end ownership from prototyping to production, delivering reliable AI-powered research tools for scientists.

The best candidates will quickly learn new scientific domains, translate AI advances into robust software, and help raise researcher productivity through scalable systems.

Qualifications

• Strong software engineering background with production systems experience.
• Hands-on depth in generative AI, AI agents, LLM apps, or RAG.
• Ability to design and ship end-to-end research tools.
• Solid CS fundamentals and problem-solving skills.
• Proven record of academic, technical, or research achievement.
• Curiosity and rapid learning across domains.

Responsibilities

• Design, build, and deploy AI agents, retrieval systems, and RAG architectures for research use.
• Take ideas from concept through prototype to production at scale.
• Create AI-powered systems to accelerate drug discovery and scientist workflows.
• Develop data pipelines, automation, and AI-assisted development processes.
• Integrate AI capabilities with complex scientific software and ML systems.
• Collaborate with engineers, researchers, and scientists on tough problems.

Skills

Software engineering

Generative AI

LLM applications

RAG architectures

AI agents

System architecture

CS fundamentals

Research achievement

Job description

Agentic AI Initiative is building a new AI program in New York, blending agentic systems, generative AI, scientific computing, and drug discovery. This software-first role emphasizes end-to-end ownership from prototyping to production, delivering reliable AI-powered research tools for scientists.

The best candidates will quickly learn new scientific domains, translate AI advances into robust software, and help raise researcher productivity through scalable systems.