Eli Lilly

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.

This role focuses on building and scaling analytical pipelines for bioinformatics and genomics data, with a significant emphasis on integrating AI-enabled tooling, including LLMs and agentic workflows. The engineer will prototype agentic workflows for automating analytical tasks, build connectors for LLM agents, and identify use cases for AI to improve research speed and quality. The role requires strong software engineering skills and a curiosity for both classical bioinformatics and modern AI.

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.

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.

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.

Principal Clinical Development AI Engineer at Eli Lilly to design, build, and deploy AI-powered solutions for clinical development workflows, partnering with statisticians and data engineers. The role involves identifying opportunities, building AI tools (including generative and multi-agent systems), establishing trust standards, and communicating results. Requires experience in AI/ML development and building adopted AI tools, with a focus on regulatory compliance and therapeutic area understanding.

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.

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.

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.

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.