We are seeking an experienced Automation Engineer to design and implement robust automation platforms that integrate scientific instrumentation, robotic automation, and laboratory data management across Lilly’s RNA Therapeutics portfolio. This role reports to the Automation Lead for RNA Therapeutics and works within a multidisciplinary automation team responsible for delivering high throughput automated systems that increase the efficiency, reproducibility, and scalability of RNA therapeutic discovery workflows. The focus of this position is the design and implementation of automation platforms and integrated workcells rather than support of individual instruments, ensuring solutions are scalable, standardized, and deployable across multiple sites. A key objective of this role is enabling Design Make Test Learn (DMTL) discovery cycles by implementing automation platforms that generate consistent, high quality experimental data with structured metadata capture. These platforms serve as experimental data generation infrastructure supporting downstream analytics and AI and machine learning model development. Automation platforms developed in this role will act as the experimental backbone of the RNA discovery DMTL cycle, enabling the generation of large, high quality datasets required to train and continuously improve AI and machine learning models. Automation platforms developed in this role will support the generation of high-quality, standardized experimental datasets with consistent metadata capture, enabling reliable downstream analytics and machine learning model training. The role requires deep expertise in liquid handling automation and experience implementing complex scientific workflows including next generation sequencing (NGS) library preparation and high throughput RNA processing. These automated workflows form a critical part of the infrastructure required to generate clean, reproducible, and unbiased datasets used to train machine learning models. The successful candidate will partner closely with scientific, AI and ML, informatics, and data engineering teams to translate biological workflows into reliable automated platforms that produce traceable, structured experimental datasets. The ability to work in a collaborative, fast moving environment and adapt to evolving scientific needs is critical.
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Job Type
Full-time
Career Level
Senior