Advisor - Agent Research

100 Eli Lilly and Company; South San Francisco (CA); On-site; USD 152,000 - 222,000; Full time

Job summary

Lilly is seeking a scientist-engineer hybrid to design the learning layer of an autonomous agent platform for molecule discovery. You will craft environments, rewards, and domain models that enable agents to improve from experimental feedback and translate wet-lab signals into trainable signals.

You will post-train domain models and integrate learned policies with RDKit, molecular graph ML, ELN/LIMS APIs, and instrument drivers to execute DMTA tasks.

Qualifications

• PhD or MS + 3 yrs / BS + 5 yrs equivalent in ML, bioinformatics, cheminformatics, CS, or related field.
• 1–2 years applying AI/ML in biology, chemistry, neuroscience, or related areas.
• Hands-on experience training or post-training AI models.

Responsibilities

• Research & Innovation: Partner with scientists to build autonomous agents for molecule discovery tasks.
• Design RL environments with real discovery tasks and proper state/action/termination semantics.
• Curate reward signals from noisy scientific data and post-train domain models.
• Integrate policies with tools like RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers.

Skills

Python

ML frameworks

Reinforcement learning

Agentic AI

Cloud architectures

Mentoring

Publications

Education

PhD or MS + 3 yrs / BS + 5 yrs equivalent

Tools

RDKit

Molecular graph ML

ELN/LIMS APIs

Kubernetes

Nextflow / Argo

AWS/Azure

Job description

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life‑changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Organization Overview

Lilly Small Molecule Discovery is an organization purpose‑built to create molecules that make life better for people. We focus on using cutting edge science to unlock new approaches that can treat people suffering from diseases with poor treatment options. We continually challenge ourselves to deliver molecules that can provide breakthrough efficacy with the highest possible safety margins. We are dedicated to optimizing our mindset, technology, and processes for faster, more nimble execution. Our success is built on a culture that empowers innovative problem solving through open collaboration and individual accountability. Discovery Technology and Platforms is a newly established function within this organization. Its mission is to accelerate molecule discovery by building highly optimized foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and intentionally investing in novel technologies and capabilities. Frontier AI is a purpose‑built team that fuses scientific agentic AI, lab automation, and unified data platforms to autonomously design, run, and refine experiments—accelerating molecule discovery.

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 design the learning layer of our scientific agent platform. You will design the environments, rewards, and domain‑specific models that enable agents to improve based on experimental feedback. You'll translate wet‑lab and computational endpoints into a trainable signal to build models that plan and act against them.

Responsibilities

• Research & Innovation: Partner with scientists to build autonomous agents that undertake molecule discovery tasks.
• Design and build reinforcement learning (RL) environments that wrap real discovery tasks with appropriate state, action, and termination semantics.
• Curate and engineer reward functions from noisy scientific signal.
• Post‑train domain models (SFT, DPO/GRPO/PPO, reward modeling, distillation) on chemistry and biology tasks.
• Integrate learned policies with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) so trained models execute real DMTA tasks.
• Build the eval infrastructure: task suites, scoring harnesses, regression tracking, and experiment tracking (e.g., MLflow).
• 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

• Trained models that measurably outperform prompted frontier baseline models on internal discovery tasks.
• Reward and evaluation infrastructure that other teams adopt as the default way to measure agent performance.
• Measurable reduction in DMTA turnaround through autonomous planning and execution.
• Seamless transition from prototype to production‑deployed AI systems.

Basic Qualifications

• PhD (or MS + 3 yrs / BS + 5 yrs equivalent experience) in Machine Learning, Bioinformatics, Cheminformatics, Computer Science, or related discipline with demonstrated wet‑lab collaboration or hands‑on experience.
• Approximately 1‑2 years of demonstrated experience in applying AI/ML in scientific disciplines such as biology, chemistry, neuroscience, or a related field (industry postdoc counts).
• Hands‑on experience training or post‑training AI models.

Additional Preferences

• Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch, Tensorflow, JAX, HuggingFace).
• Experience with RL and post‑training methods (PPO, GRPO, DPO, reward modeling, RLHF/RLAIF) and libraries such as TRL, verl, or equivalent in‑house stacks.
• Familiarity with molecular representation learning, generative chemistry, or protein/nucleic acid models.
• Hands‑on experience building agentic AI systems (e.g., OpenAI/ Anthropic Agent SDK, Langchain, Smol agents).
• Experience designing and shipping end‑to‑end systems in cloud environments (backend APIs, lightweight frontends, and agentic platforms) - GitHub portfolio a plus.
• Working knowledge of cloud‑native (AWS/Azure) pipeline architectures, including Nextflow, Argo on Kubernetes.
• 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.

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 (AMECA), Black Employees at Lilly (BE@Lilly), Chinese Culture Network (CCN), EnAble, Evolve, Lilly Indian Network (LIN), Organization of Latinx at Lilly (OLA), Pride (LGBTQ+ Allies), Veterans Leadership Network (VLN) and Women’s Initiative for Leading at Lilly (WILL).

Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is $151,500 - $222,200 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.

#WeAreLilly At Lilly we strive to ensure our employees are part of a team that cares about them and our shared purpose of making life better for those around the world. How do we do this? We continue to look for ways to include, innovate, accelerate and deliver while maintaining integrity, excellence and respect for people. We hope that you seek to join us on our journey as we create medicine and deliver improved outcomes for patients across the globe! #WeAreLilly