Advisor - Agent Research

Role Summary

We are rebuilding the Design-Make-Test-Analyze (DMTA) cycle, infusing scientific automation with foundation models, multi-agent systems, and robotics to make scientific discovery intelligent, autonomous, and fast. We're seeking a scientist-engineer hybrid to deploy AI-driven discovery platforms directly with portfolio research teams. You'll bridge the gap between cutting-edge agentic AI systems and real-world drug discovery workflows.

Responsibilities

• Partner with chemists and biologists to translate scientific workflows into agentic systems
• Deploy and integrate Agentic AI system into active research programs
• Design and implement cloud-native data pipelines connecting lab instruments, databases, and AI models
• Support model deployment, inference services, and experiment tracking (e.g., MLflow)
• Integrate LLM reasoning with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to build composite agents that plan, simulate, and execute DMTA tasks
• Prototype and iterate rapidly on agent planning strategies, memory systems, and human-in-the-loop patterns

• Represent Frontier AI in the broader AI@Lilly and external AI research community: publish, give talks, review papers, and scout emerging trends.
• Evaluate external vendors, open-source projects, and academic collaborations for strategic fit.

• Measurable reduction in DMTA turnaround through autonomous planning and execution
• Seamless transition from prototype to production-deployed AI systems

Qualifications

• Required: PhD (or MS + 2 yrs / BS + 4 yrs equivalent experience) in Bioinformatics, Cheminformatics, Computer Science, or related discipline with demonstrated wet-lab collaboration or experience.
• Required: Approximately 1-2 years of demonstrated experience of applying AI/ML in scientific discipline such as biology, chemistry, neuroscience, or a related field (industry postdoc counts)
• Preferred: Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch, Tensorflow, JAX, HuggingFace).
• Preferred: Hands-on experience building agentic AI systems (e.g., LangChain, OpenAI Agents SDK)
• Preferred: Experience designing and shipping end-to-end systems in cloud environments (backend APIs, lightweight frontends, and agentic platforms) - GitHub portfolio a plus
• Preferred: Strong DevOps/engineering skills: version control (git), containerization (docker, kubernetes), GitOps + CI/CD practices, data systems (Redis, SQL/NoSQL), unit testing, frontend (streamlit, flask)
• Preferred: Working knowledge of cloud-native (AWS/Azure) pipeline architectures including Nextflow, Argo on Kubernetes
• Preferred: Familiarity with MLOps, including model versioning, data versioning, and continuous integration/continuous deployment for ML systems.
• Preferred: Experience with LLM post-training, fine-tuning, or RLHF
• Preferred: Demonstrable research experience, evidenced by contributions to projects, and ideally through publications in relevant ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).
• Preferred: Experience mentoring and guiding junior researchers or engineers.

Skills

• Python proficiency; deep experience with ML/Deep Learning frameworks (e.g., PyTorch, TensorFlow, JAX, HuggingFace)
• Hands-on experience building agentic AI systems (e.g., LangChain, OpenAI Agents SDK)
• Experience designing and shipping end-to-end systems in cloud environments (backend APIs, lightweight frontends, and agentic platforms) - GitHub portfolio a plus
• Strong DevOps/engineering skills: version control (git), containerization (Docker, Kubernetes), GitOps + CI/CD practices, data systems (Redis, SQL/NoSQL), unit testing, frontend (Streamlit, Flask)
• Working knowledge of cloud-native (AWS/Azure) pipeline architectures including Nextflow, Argo on Kubernetes
• Familiarity with MLOps, including model versioning, data versioning, and CI/CD for ML systems
• Experience with LLM post-training, fine-tuning, or RLHF
• Demonstrable research experience, evidenced by contributions to projects, and ideally through publications in ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)
• Experience mentoring and guiding junior researchers or engineers

Education

• PhD (or MS + 2 yrs / BS + 4 yrs equivalent) in Bioinformatics, Cheminformatics, Computer Science, or related discipline