Advisor - Agent Research

Role Summary

Advisor - Agent Research at Lilly focuses on deploying AI-driven discovery platforms within portfolio research teams to bridge cutting-edge agentic AI systems with real-world drug discovery workflows. The role aims to infuse design-make-test-analyze cycles with automation, foundation models, multi-agent systems, and robotics to accelerate scientific discovery and reduce DMTA turnaround.

Responsibilities

• Partner with chemists and biologists to translate scientific workflows into agentic systems
• Deploy and integrate Agentic AI systems 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 applying AI/ML in scientific disciplines such as biology, chemistry, neuroscience, or 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: git, Docker/Kubernetes, GitOps + CI/CD, 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 CI/CD for ML
• Preferred: Experience with LLM post-training, fine-tuning, or RLHF
• Preferred: Demonstrable research experience with publications in ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)
• Preferred: Experience mentoring and guiding junior researchers or engineers

Skills

• Python and ML/Deep Learning frameworks (PyTorch, TensorFlow, JAX, HuggingFace)
• Agentic AI systems (e.g., LangChain, OpenAI Agents SDK)
• Cloud architectures for backend APIs, frontends, and agentic platforms
• DevOps and software engineering practices (Git, Docker, Kubernetes, CI/CD)
• Data systems (Redis, SQL/NoSQL), testing, and lightweight frontends (Streamlit, Flask)
• Cloud-native pipelines (AWS/Azure) with Nextflow, Argo on Kubernetes
• MLOps concepts including model/data versioning and CI/CD for ML
• LLM customization: post-training, fine-tuning, RLHF
• Research experience with contributions/publications in ML/NLP venues
• Mentoring and guiding junior team members

Education

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