Lilly is looking to accelerate drug discovery by building a next-generation platform that leverages agentic AI and LLMs to improve processes from molecular hypothesis generation to experimental design optimization.
Requirements
Expertise in Python and proficiency in one or more of the following: Node.js, Go, Java, or Rust.
Experience with scientific computing libraries (e.g., NumPy, SciPy, Pandas, BioPython) and omics analysis frameworks is highly desirable.
Hands-on experience building or scaling platforms in cloud-native environments (AWS, GCP, or Azure) using container orchestration (Kubernetes) and infrastructure-as-code tools like Terraform—preferably for computationally intensive biological workflows such as genome assembly or protein structure prediction.
Familiarity with frontend development using React or similar frameworks, ideally applied to scientific data visualization or interactive analytics for complex biological datasets.
Strong interest in applying software engineering to scientific discovery, which includes areas such as: LLM-powered scientific reasoning for hypothesis generation, literature mining, and protocol optimization; AI-driven target identification and CRISPR screening analysis; Real-time experimental design optimization using active learning; Agentic AI systems for orchestrating multi-step, tool-enabled scientific workflows; High-throughput pipeline development for GWAS, single-cell, or multi-modal omics studies; Scientific visualization for networks, pathways, and drug mechanisms; Feedback loops that connect wet-lab automation with real-time AI-guided experimentation; Multi-omics data integration (e.g., scRNA-seq + ATAC-seq, spatial transcriptomics, proteomics/metabolomics co-analysis).
7+ years of experience in software engineering with a track record of delivering robust, scalable platforms, with demonstrated experience in scientific computing environments supporting biological data analysis or experimental workflows.
Demonstrated ability to lead engineering projects from architecture to production, ideally in scientific or research environments involving complex biological datasets and multi-step experimental protocols.
Responsibilities
Design, implement, and scale key components of an agentic AI platform supporting drug discovery workflows, including target-disease association discovery, compound-protein interaction prediction, and multi-omics biomarker identification.
Build modular backend services and orchestration layers that can support LLM-powered literature mining, real-time experimental planning, and tool-use chains over scientific data including single-cell RNA-seq, spatial transcriptomics, CRISPR screens, and proteomics datasets.
Collaborate with AI scientists and computational biologists to integrate LLM frameworks (e.g., LangTorch, Semantic Kernel) with structured biological data sources to enable reasoning across multi-omics, assay data, protein-protein interaction networks, metabolic pathways, and experimental results from high-throughput screens.
Develop intelligent interfaces using React or similar frameworks to support interactive, AI-guided workflows for target identification, pathway enrichment analysis, drug-target network exploration, CRISPR hit validation, and automated experimental protocol generation.
Ensure data integrity, security, and traceability across workflows that handle omics, compound, assay data, and sensitive biological datasets including patient-derived samples, with proper provenance tracking for regulatory compliance.
Lead development of scalable services deployed via Kubernetes and Terraform in cloud environments (AWS, GCP) optimized for high-throughput computational biology workloads including genome-wide association studies and molecular dynamics simulations.
Apply CI/CD, test automation, and observability to enable robust, maintainable deployment pipelines for scientific computing environments supporting real-time experimental feedback and automated hypothesis testing.
Other
Proven experience as a senior software engineer or tech lead, with a strong foundation in backend architecture, microservices, and distributed systems—ideally in scientific computing platforms supporting multi-omics data or high-throughput biological assays.
Strong communication skills and a collaborative mindset for partnering with cross-functional teams including computational biologists, structural biologists, chemical biologists, and lab scientists.
Intellectual curiosity and a growth mindset—especially an eagerness to deepen your understanding of systems biology, experimental design, and AI applications in drug discovery.
Bachelor's or Master's degree in Computer Science, Software Engineering, Bioinformatics, Computational Biology, Systems Biology, or a related technical field with coursework in molecular biology, genetics, or biochemistry.
