Eli Lilly
Pharma · Pharma
- HQ
- Indianapolis, US
- Founded
- 1875
- Size
- 35,000+
- Website
- lilly.com
Currently tracking 28 active AI roles, down 25% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $58k–$345k (avg $175k).
Eli Lilly currently has 36 active AI-related roles in our index. The most common open titles are: Director, Discovery Bioinformatics Oncology (2), Advisor - Agent Research, Advisor - Antibody Developability Validation & Benchmarking, Advisor - Data Architect, Data Foundry, Advisor - Lab Automation Software Engineer. Most positions are in Engineering and Research.
Eli Lilly's active AI hiring is concentrated in: agents (53%), data (25%), application (8%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Eli Lilly is hiring AI talent in: United States (29 roles), India (7 roles).
Job postings at Eli Lilly most frequently reference: agent orchestration, rag, model serving, llm observability, vector db.
In the past 30 days, Eli Lilly has posted 5 new AI-related roles. That is a -78% change versus the prior 30 days (23 → 5).
Eli Lilly currently has 36 active AI-related job listings. The majority of these roles, 56%, are focused on agents, with data-related positions making up another 28%. Engineering is the most frequent function for these hires. The company is primarily hiring in the United States and India. Frequent technology tags include agent orchestration, RAG, and model serving, indicating a focus on building and deploying AI systems.
| Title | Stage | AI score |
|---|---|---|
| Advisor - Agent Research Seeking a scientist-engineer hybrid to deploy AI-driven discovery platforms using foundation models, multi-agent systems, and robotics in drug discovery workflows. The role involves translating scientific workflows into agentic systems, integrating LLM reasoning with domain tools, and supporting model deployment and inference services. | AgentServe | 9 |
| Advisor - Antibody Developability Validation & Benchmarking The Advisor - Antibody Developability Validation & Benchmarking role at Eli Lilly is part of the AI-powered drug discovery platform, Lilly TuneLab. This role focuses on validating federated antibody models by building benchmark suites, designing test sets, integrating public benchmarks, and developing validation frameworks. The goal is to ensure the trustworthiness of AI models for triaging drug candidates, partnering closely with modeling scientists on design choices and statistical rigor, and integrating validation into the MLOps pipelines. | Eval GatePost-train | 8 |
| Director, Discovery Bioinformatics Oncology Lead the AI/ML innovation & deployment for oncology discovery, architecting and operationalizing state-of-the-art machine learning (deep learning, foundation models, LLM-powered applications) to accelerate target identification, protein/antibody design, and multimodal data integration. Develop next-gen data integration platforms, advance computational protein & antibody design using active learning, and design/oversee experiments. Deliver robust, scalable ML systems with MLOps on cloud platforms and foundational bioinformatics. | AgentData | 8 |
| Applied AI Engineer, Clinical Informatics This role focuses on building agentic AI applications and ML systems to extract, define, and contextualize patient phenotypes from clinical trial datasets, biobank data, and electronic health records. It involves developing RAG pipelines, applying unsupervised/self-supervised learning, survival models, and NLP techniques to derive translational insights for clinical research. The role requires strong Python/R skills and experience with cloud environments, with a focus on research rigor and reproducibility. | AgentData | 8 |
| Sr. Principal or Engineering Advisor - Agentic Lab Automation Integration This role focuses on engineering the integration between agentic AI and physical lab systems, specifically for automating and accelerating molecule discovery in a healthcare setting. The engineer will build multi-agent systems, design agent workflows that interact with lab automation platforms, and deploy these systems into production environments. The goal is to create autonomous agents that can execute experiments, reduce experimental cycle times, and become integral to lab operations. | Agent | 8 |
| Director, Discovery Bioinformatics Oncology Lead the AI/ML innovation & deployment for oncology discovery, architecting and operationalizing state-of-the-art machine learning models (deep learning, foundation models, LLMs) to accelerate target identification, protein/antibody design, and multimodal data integration. Develop next-gen data integration platforms, advance computational protein & antibody design using active learning, and design/oversee experiments. Deliver robust, scalable ML systems with MLOps on cloud platforms and ensure foundational bioinformatics practices. | AgentData | 8 |
| Advisor - Software Engineer Advisor Software Engineer (R5) role focused on designing and building agentic AI systems, intelligent automation pipelines, and next-generation software platforms. This is a hands-on technical leadership role involving system design, full-stack engineering, and championing AI-driven development practices within a healthcare company. | Agent | 8 |
| Scientific Lead - Forward Deployed AI Engineer, Applied Intelligence for Discovery The Forward Deployed AI Engineer will embed with research teams to translate scientific use-cases into production AI systems for drug discovery. This role involves applying LLMs, RAG, and agentic frameworks to solve scientific problems, running evaluation loops, and distilling learnings into reusable components. The engineer will own end-to-end deployments, ensuring reliability and integrating with AI/LLMOps platforms, with a focus on measurable workflow impact and evidence-based feedback loops. | AgentServe | 8 |
| Associate Vice President - Methods4Insight, Data Foundry Associate Vice President leading the analytical methods and computational science pillar within Data Foundry, focusing on accelerating molecule discovery. This role involves leading a team of experts in cheminformatics, computational structural biology, statistical modeling, and AI/ML, ensuring Lilly has access to advanced analytical approaches for both human scientists and AI agents. The position requires strategic decision-making on adopting or developing methods, identifying data gaps, and establishing validation frameworks, with a strong emphasis on integrating methods into AI agent workflows. | DataAgent | 8 |
| Software Engineer, AI & Clinical Applications Software Engineer role focused on building AI-powered and agentic applications to automate clinical workflows within a regulated healthcare environment. The role involves full-stack development, architecting LLM-integrated tools, and ensuring compliance with industry standards. | Agent | 7 |
| Engineer - MLOps & Scientific Platforms - Data Foundry Engineer focused on MLOps and scientific platforms to operationalize AI/ML tools for drug discovery. Responsibilities include building ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails to make scientific methods reliable and scalable for both human scientists and autonomous AI agents. This role bridges methods development and data infrastructure, ensuring tools are analytics-ready, monitored, and exposed via APIs with performance guarantees. | ServeAgent | 7 |
| Post Doctoral Scientist – Human Genomics and Translational Data Sciences This role focuses on applying statistical and computational approaches, including machine learning, to analyze large-scale multi-omics (genomic, proteomic, metabolomic) and clinical data from biobanks and population cohorts. The goal is to identify novel therapeutic targets and biomarkers for cardiometabolic diseases. The role involves developing and implementing bioinformatics pipelines, contributing to novel statistical methods, and collaborating with interdisciplinary teams to guide therapeutic development. While the primary focus is on data analysis and method development (L0/L2), the ultimate aim is to inform drug discovery and development. | DataPost-train | 7 |
| Director – Software Product Management, Enterprise AI Orchestration The Director of Software Product Management for Enterprise AI Orchestration at Eli Lilly will define and drive the strategy for deploying, managing, and optimizing AI agents and workflows at an enterprise scale within the pharmaceutical industry. This role involves owning the product roadmap for an enterprise AI orchestration platform, enabling business units to build and manage AI workforces, and ensuring AI investments deliver measurable business value while adhering to compliance requirements. | Agent | 7 |
| Director/Senior Director, Analytical Methods (Methods4Insight) Director/Senior Director, Analytical Methods role within Lilly's Data Foundry team, focusing on developing and deploying cutting-edge analytical methods (cheminformatics, computational structural biology, statistical modeling, AI/ML) to accelerate molecule discovery. This role involves translating advanced methods into practical capabilities for both human scientists and autonomous AI agents, collaborating with other Data Foundry pillars and Frontier AI, and establishing best practices for method validation and impact tracking. | Agent | 7 |
| Sr. Staff Software Engineer - AI Chat Senior Staff Software Engineer to own the technical direction and delivery of ChatNow's core platform, which includes the routing engine that matches intent to the right agent, the integration layer that connects a growing ecosystem of AI capabilities, and the experience layer that makes it all feel effortless. The role involves defining the architecture and driving implementation of the conversation routing engine, owning the agent integration layer, building streaming chat interfaces, and integrating LLM capabilities through a model gateway with multi-model routing and fallback. | Agent | 7 |
| CADD Postdoctoral Scientist Postdoctoral Scientist role focused on developing synthesis-aware virtual screening workflows for early small-molecule drug discovery, integrating AI/ML with structure-based drug design and fragment chemistry. | Data | 7 |
| Technical Lead - Software Developer, Data Foundry Scientific Software Developer to build software systems for AI-native drug discovery. This role involves creating prototypes, data pipelines, APIs, MLOps infrastructure, agentic platform components, and lab automation integrations. The work spans architecture, methods, and automation, with a focus on a prototype-to-production model, handing off mature solutions to an enterprise scaling team. Responsibilities include building data pipelines for scientific datasets, developing APIs for LIMS and instruments, implementing MLOps for model deployment and observability, developing agent-ready APIs and infrastructure for closed-loop experimentation, integrating lab automation, and building cloud-native components with DevSecOps practices. | AgentServe | 7 |
| Director, Software Product Management – Discovery Research Platforms Product Management leader to shape strategy and development of custom software platforms for discovery research, focusing on computational drug design and optimization for large molecules. The role involves integrating agentic AI capabilities into research workflows, accelerating design-make-test-learn cycles, and enabling scientists to compose complex computational pipelines through AI-assisted interfaces. Requires partnership with computational biologists, protein engineers, and engineering teams to translate research needs into scalable software platforms. | Agent | 7 |
| Director/Senior Director, ADMET & PK/PD Modeling This role focuses on building and validating predictive models for ADMET and PK/PD endpoints within a healthcare AI/ML platform for drug discovery. It involves developing translational and distribution modeling approaches, ensuring model usability for partners, defining data strategies, and leveraging AI/agentic tools for automation and interpretability. The role also includes mentorship of junior scientists. | Data | 7 |
| Scientific Lead - Scientific Data Engineer This role focuses on building the data infrastructure and semantic layer to make scientific data accessible for AI systems, specifically for drug discovery research. It involves designing and building data architectures, ETL/ELT pipelines, and AI-ready data products, including vector embedding pipelines for RAG. The role bridges data infrastructure and generative AI engineers, aiming to convert early deployments into repeatable system standards and evaluation practices. | DataAgent | 7 |
| Senior/ Principal - Manufacturing Data Scientist This role focuses on delivering advanced analytics and predictive modeling for pharmaceutical manufacturing and development processes. The Senior/Principal Data Scientist will build, validate, and maintain statistical models for real-time process monitoring, anomaly detection, and performance optimization. They will collaborate with cross-functional teams, IT, and global stakeholders to deploy secure, scalable analytics solutions, ensuring data integrity and compliance with GMP and regulatory standards. The role involves data integration, visualization, and driving digital transformation within a regulated healthcare environment. | ServeData | 7 |
| Product Manager - GenAI Product Manager for GenAI products at Eli Lilly, focusing on defining vision, strategy, and execution. Requires experience with GenAI concepts, agent evaluation, and AI tools, bridging user needs with technical delivery in a healthcare context. | Agent | 7 |
| Principal Engineer - Quality Engineering Principal Engineer - Quality Engineering role focused on designing and building evaluation frameworks for AI platforms (LLM chat/voice bots) and robust test automation for API, web, and mobile applications. Requires hands-on Python, Java, JavaScript/TypeScript for test infrastructure, CI/CD integration, and advanced AI testing methodologies. Also involves performance testing and cloud-based execution. | Eval GateAgent | 7 |
| Senior Principal Software Engineer Senior Principal Software Engineer to join the AI & Platform team, focusing on designing, building, and scaling intelligent systems using Generative AI and LLMs. The role involves end-to-end delivery of GenAI/LLM-powered platform capabilities, designing multi-agent AI architectures, and working across backend and frontend layers. Requires strong software engineering skills, cloud-native architecture experience, and familiarity with agentic AI frameworks. | Agent | 7 |
| Director/Senior Director/ Executive Director: Digital Innovation & Technology Strategy – Global PK/PD & Pharmacometrics This role focuses on developing and implementing a digital strategy for integrating AI, ML, and automation into quantitative pharmacology and pharmacometrics workflows within a healthcare setting. The goal is to accelerate drug development, position the organization as an industry leader in AI-enabled drug development, and ensure scientific rigor and regulatory compliance. The role involves evaluating emerging AI technologies, leading pilots, partnering with IT and data science teams, driving AI adoption through organizational change, and defining success metrics. | Ship | 7 |
| Advisor, Data Scientist - CMC Data Products The role focuses on developing and delivering enterprise-scale data products that power AI-driven insights, process optimization, and regulatory compliance within the pharmaceutical domain. It involves defining data archetypes, creating reusable data models, and implementing data frameworks for regulated environments. The core responsibility is building AI-ready data products, including training datasets for various AI/ML applications and supporting generative AI for knowledge management. | Data | 7 |
| Advisor - Lab Automation Software Engineer This role focuses on designing and building AI-integrated, closed-loop autonomous discovery ecosystems for biotherapeutics research. It involves orchestrating automated laboratory workflows, implementing ML models for optimization, and creating digital lab infrastructure. The primary output is an agentic system that proposes hypotheses, executes experiments, analyzes results, and iteratively refines designs. | AgentData | 7 |