Advisor - Agent Research

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We're looking for people who are determined to make life better for people around the world.

Position 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:

Research & Innovation

• 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

External Engagement

• 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.

What Success Looks Like

• Measurable reduction in DMTA turnaround through autonomous planning and execution

• Seamless transition from prototype to production-deployed AI systems

Basic Qualifications:

• 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.

• 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)

Additional Preferences:

• Proficiency in Python and 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 continuous integration/continuous deployment 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 relevant ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).

• Experience mentoring and guiding junior researchers or engineers.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women's Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.

Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is

$151,500 - $244,200

Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.

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