Advisor - Agentic Automation, Frontier AI

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. Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley! Making medicine that’s never been made means doing what’s never been done. If you’re an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation, we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us! About the Lilly and NVIDIA Partnership Lilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery’s toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe. Position Summary We are rebuilding the Design-Make-Test-Analyze (DMTA) cycle by integrating agentic AI, cloud-based orchestration, and LIMS infrastructure to connect experimental readouts with the tools that accelerate laboratory science. You will engineer the connective tissue between agentic AI and physical lab systems building practical integrations with robotic platforms, analytical instruments, and data pipelines. You will design agent workflows that reason over experimental data, trigger automated actions, and surface insights to scientists. This is an on-site hands-on individual contributor role: you will prototype rapidly, productionize what works, and collaborate with chemists, biologists, and automation engineers to deploy intelligent systems that accelerate molecule discovery. This position reports to the Agentic Automation Lead. You will own significant components of the agentic automation platform and be trusted to make technical decisions within them. Key Responsibilities Integration Engineering Build and maintain the translation layer between high-level agent planning logic and low-level instrument control across lab automation platforms (Hamilton, Tecan, Opentrons) and analytical instruments (LC/MS, NMR, HPLC) Connect experimental readouts to ELN/LIMS and downstream data pipelines so every run produces model-ready, traceable data Agent Development Build multi-agent systems with robust orchestration, state management, error recovery, and tool integration Prototype and iterate rapidly on agent planning strategies, memory systems, and human-in-the-loop patterns Solution Deployment Partner with automation engineers and scientists to transition prototypes into reliable lab operations Deploy and maintain containerized services using Docker and Kubernetes with GitOps and CI/CD practices Integrate cloud-based orchestration frameworks such as Argo on Kubernetes with laboratory control systems Collaboration & External Engagement Work directly with NVIDIA engineers and researchers and with Lilly scientists in the co-innovation lab Evaluate open-source projects, vendor tooling, and academic work for practical fit What Success Looks Like Autonomous agents reliably execute multi-step experiments on physical laboratory instruments Measurable reduction in DMTA turnaround through autonomous planning and execution Basic Qualifications PhD (or MS + 3 yrs / BS + 5 yrs equivalent experience) in Chemical / Mechanical Engineering, Robotics, Computer Science, Computer Engineering, Chemistry, or a related discipline, with demonstrated wet-lab automation experience Direct experience integrating software control and/or AI systems with lab automation platforms (liquid handlers, analytical instruments, robotic workflows) Preferred Qualifications Strong experience with containerization (Docker) and Kubernetes-based orchestration in production environments Experience building scalable, production-grade Python applications using tools such as Redis, FastAPI, Flask/Streamlit, and pytest (GitHub portfolio a plus) Hands-on experience building LLM agent or multi-agent systems that call real tools in production Experience with LLM post-training, fine-tuning, or RLHF Experience with self-driving labs, autonomous experimentation, or high-throughput experimentation (HTE) workflows Proven ability to build and maintain the translation layer between high-level planning logic and low-level instrument control Familiarity with the NVIDIA stack for life sciences (BioNeMo, CUDA, Omniverse) Experience mentoring interns, students, or junior engineers (this is a growth path, not a requirement) Demonstrable research experience, evidenced by contributions to projects and ideally through publications in relevant ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR) or leading chemistry journals This is an onsite position based in South San Francisco, CA. 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