Organization OverviewLilly 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 SummaryWe are rebuilding the Design-Make-Test-Analyze (DMTA) cycle by integrating agentic AI, cloud-based orchestration, and LIMS infrastructure to connect experimental readouts with 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'll design agent workflows that reason over experimental data, trigger automated actions, and surface insights to scientists. This is a hands-on engineering role: you'll prototype rapidly, productionize what works, and collaborate with chemists, biologists, and automation engineers to deploy intelligent systems that accelerate molecule discovery tasks.
Research & Innovation- 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
- Design agent architectures that interface with lab automation platforms (Hamilton, Tecan, Opentrons) for closed-loop experimental execution
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
External Engagement- Represent Frontier AI in the broader AI[redacted] 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- Autonomous agents reliably execute experiments on physical laboratory instruments
- Measurable reduction in DMTA turnaround through autonomous planning and execution
- Agents you build become indispensable to the automation engineering community
- Frontier AI platforms scale from pilot deployments to production lab environments
Basic Qualifications- PhD (or MS + 2 yrs / BS + 5 yrs equivalent experience) in Chemical / Mechanical Engineering, Robotics, Computer Engineering, or related discipline with demonstrated wet-lab automation experience.
- Demonstrated experience with laboratory automation systems and LIMS engineering
- Direct experience integrating software control and/or AI systems with lab automation platforms (liquid handlers, analytical instruments,robotic workflows)
- Strong experience with containerization (Docker) and Kubernetes-based orchestration in production environments
- Experience building scalable and production-level python applications using tools like Redis, FastAPI, flask/streamlit, pytest, etc. (GitHub portfolio a plus)
Preferred- Experience with LLM post-training, fine-tuning, or RLHF
- Direct experience integrating AI or algorithmic decision systems with laboratory automation
- Proven ability to build and maintain the translation layer between high-level planning logic and low-level instrument control
- 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
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$87,000 - $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.
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