Advisor - Agent Research

Eli Lilly and Company • San Francisco, CA

10d • Onsite

About The Position

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. 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. 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.

Requirements

• 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

Nice To Haves

• 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.

Responsibilities

• 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)
• 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.

Benefits

• company bonus (depending, in part, on company and individual performance)
• company-sponsored 401(k)
• pension
• vacation benefits
• 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
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree