Position SummaryWe are looking for an experienced Automation AI Scientist to design and build the agentic systems that plan, execute, and learn from physical experiments. You will own the decision layer of the closed-loop lab: the agents that reason over experimental context, select the next experiment, dispatch it to robotic and analytical platforms, interpret the readout, and update their own strategy - with scientists in the loop at the points where judgment matters.
This is an on-site hands-on scientific engineering role with technical leadership scope. You will prototype rapidly against real instruments and real chemistry, productionize what works, and partner with chemists, biologists, automation engineers, and our NVIDIA counterparts to compress Design-Make-Test-Analyze cycle time from weeks to days. You will also lead a small team of scientist-engineers, set the technical direction for the agentic automation platform, and be accountable for it as a product that other teams depend on - not a collection of prototypes.
Key ResponsibilitiesAgentic System Design- Design and build multi-agent systems with robust orchestration, state management, error recovery, and tool integration for laboratory workflows
- Build the translation layer between high-level scientific intent and low-level instrument control across automation platforms (Hamilton, Tecan, Opentrons) and analytical instruments (LC/MS, NMR, HPLC)
Closed-Loop Science- Implement active learning, Bayesian optimization, and reinforcement-learning approaches for autonomous experiment selection under real-world cost and throughput constraints
- Instrument the loop end-to-end so every experiment produces model-ready data in ELN/LIMS and downstream training corpora
Solution Deployment- Partner with automation engineers and scientists to transition prototypes into reliable, monitored lab operations
- Deploy and maintain containerized services using Docker and Kubernetes with GitOps and CI/CD practices
Technical Leadership & Platform Ownership- Lead and grow a small team of scientist-engineers: set technical direction, run design and code review, and hold the bar on engineering quality
- Own the agentic automation platform end to end: roadmap, architecture, reliability, adoption, and deprecation decisions and be accountable for the outcomes it delivers to scientists
- Translate scientific and business priorities into a sequenced technical plan, and communicate progress, trade-offs, and risk to senior R&D leadership
Collaboration & External Engagement- Work directly with NVIDIA researchers and engineers and with partner teams across the AI@Lilly community
- Evaluate external vendors, open-source projects, and academic collaborations for strategic fit
What Success Looks Like- Autonomous agents reliably execute multi-step experiments on physical laboratory instruments without babysitting
- Measurable reduction in DMTA turnaround attributable to autonomous planning and execution
- The team you lead grows in capability and autonomy, with people you mentor taking ownership of major platform components
Basic Qualifications- PhD (or MS + 3 yrs / BS + 6 yrs equivalent experience) in Computer Science, Chemical Engineering, Robotics, Chemistry, Computational Biology, or a related discipline
- 5+ years of applied experience building AI or algorithmic decision systems, including production LLM agent or multi-agent systems
- At least one year of direct people management experience
Preferred Qualifications- Demonstrated experience integrating software control and/or AI systems with laboratory automation platforms (liquid handlers, analytical instruments, robotic workflows)
- Track record of owning a platform or product end to end: defining the roadmap, shipping it to real internal or external users, operating it in production, and iterating based on adoption and feedback
- Track record with self-driving labs, autonomous experimentation, or high-throughput experimentation (HTE) workflows
- Working knowledge of LIMS/ELN engineering and scientific data models in a regulated or GxP-adjacent environment
- Experience hiring and building a team from an early stage in a startup, skunkworks, or newly formed function
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$193,500 - $338,800
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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