Eli Lilly and Company

Lead the end-to-end quality engineering lifecycle for software products, combining test strategy, automation leadership, release readiness, and hands-on testing. This role leads a globally distributed QE team and drives modern, AI-assisted testing practices across multiple delivery teams.

Lead the end-to-end quality engineering strategy for software products, combining hands-on test automation with distributed team leadership. Drive reliable releases through advanced testing practices, AI-assisted testing, and cross-functional risk management.

Build and operate large-scale ML platforms powering AI-driven drug discovery at Lilly’s Silicon Valley AI co-innovation lab. You’ll enable reliable model deployment, inference, monitoring, retraining, and production operations across cloud and GPU infrastructure.

Principal Platform Engineer responsible for building and operating AWS-based research data platform foundations, while developing AI agents and MCP integrations that improve workflows for scientists and data engineers. The role combines DevOps ownership, platform reliability, and applied AI automation.

Lead hands-on development of AI and machine learning systems that turn completed clinical trials, biobank data, omics, and longitudinal health records into translational insights. The role combines clinical informatics research, biomedical data engineering, and applied AI for drug discovery.

Senior Machine Learning Engineer building and operating production-grade ML/AI systems, platforms, and deployment workflows at Lilly. The role combines hands-on ML engineering with MLOps, cloud infrastructure, monitoring, and platform reliability.

Join Lilly’s AI Engineering team to build and operate production-grade machine learning and generative-AI platforms at enterprise scale. This hands-on role combines ML engineering, MLOps, cloud deployment, and platform reliability.

Lead the architecture and delivery of production-grade ML and agentic AI systems supporting Lilly’s global commercial analytics initiatives. This hands-on advisor role combines advanced ML engineering, LLMOps, and technical leadership across engineering and data science teams.