AI Scientist

AI Scientist – Agentic AI for Drug Discovery

PhD & Postdoctoral Opportunities

The Role

We are seeking an AI Scientist to develop next-generation agentic AI systems for drug discovery. This role sits at the intersection of artificial intelligence, medicinal chemistry, and scientific research. You will help build AI agents that can reason through complex scientific problems, use specialized tools, and support iterative decision-making across the drug discovery lifecycle.

This is an ideal opportunity for PhD graduates and postdoctoral researchers who combine deep scientific expertise with hands-on experience in large language models and AI agent development.

What You’ll Do

• Design and build an agentic AI framework for drug discovery, covering critical workflows such as scientific literature retrieval, target analysis, synthetic route proposal, experimental design, and iterative analysis of experimental results.
• Develop an AI-powered “digital medicinal chemist”—an interactive, agentic workspace that can assist with or autonomously execute scientific reasoning and experimental decision-making tasks in small-molecule drug discovery.
• Collaborate closely with model post-training teams to translate scientific expertise into training data, preference data, reward signals, and evaluation benchmarks.
• Build a closed feedback loop between application-layer scientific challenges and foundational model improvement, ensuring that real-world drug discovery needs directly inform model development.
• Drive the application of AI models across key stages of drug development, including target-to-hit, hit-to-lead, lead optimization, and preclinical candidate selection, with opportunities to expand into biologics and other life science applications.
• Develop rigorous evaluation methods for scientific agents, including assessments of reasoning quality, tool use, factual accuracy, experimental feasibility, and practical research impact.
• Publish high-quality research papers and technical reports, and contribute to the broader scientific community through leading conferences, workshops, and preprint platforms.

Minimum Qualifications

• PhD or postdoctoral training in chemistry, biology, pharmaceutical sciences, materials science, or a related discipline.
• Strong domain knowledge in drug discovery, medicinal chemistry, molecular design, or a closely related scientific field.
• Hands-on experience developing or applying LLM-based agents, including technologies such as tool use/function calling, prompt engineering, retrieval-augmented generation (RAG), and multi-step agent workflows.
• Demonstrated experience independently designing or building an AI agent, scientific software system, or LLM-powered application.
• Ability to translate complex scientific questions into clearly defined AI tasks, datasets, evaluation criteria, and technical workflows.
• Strong Python programming skills and practical experience with deep learning frameworks such as PyTorch.
• Experience implementing an end-to-end model development workflow, including model training, fine-tuning, evaluation, or deployment.
• Strong communication and collaboration skills, with the ability to work effectively across scientific research, machine learning, and engineering teams.

Preferred Qualifications

• Publications in drug discovery, molecular design, protein engineering, AI for Science, or related fields, particularly in leading scientific journals or top-tier AI/ML conferences.
• Experience fine-tuning large language models using methods such as LoRA, full-parameter fine-tuning, or instruction tuning.
• Experience with reinforcement learning or preference optimization methods, such as RLHF, RLAIF, DPO, or GRPO.
• Experience developing multimodal models, multi-agent systems, scientific foundation models, or autonomous research agents.
• Familiarity with cheminformatics, computational chemistry, molecular modeling, reaction prediction, retrosynthesis, or laboratory automation.
• Experience working with real-world drug discovery data or collaborating with medicinal chemists, biologists, or experimental scientists.

What We Offer

• The opportunity to work on a core strategic initiative sponsored directly by our CEO.
• Significant research autonomy, strong organizational support, and access to interdisciplinary scientific expertise.
• A chance to help define the future of agentic AI and AI-enabled drug discovery at a globally competitive AI-for-Science company.
• Access to advanced computing infrastructure, including H100- and H200-class GPU clusters.
• Authorship opportunities on technical reports, research publications, and other public-facing scientific contributions based on individual contributions.
• Support for publishing cutting-edge research and participating in leading academic and industry conferences.
• Opportunities to explore internal innovation, technology incubation, and entrepreneurial initiatives.