Fulltime Molecular Simulation Jobs (NOW HIRING)

Job Summary:
Eli Lilly and Company is a global healthcare leader headquartered in Indianapolis, Indiana. They are seeking an Engineer - MLOps & Scientific Platforms - Data Foundry to operationalize scientific tools and analytical methods into actionable prototypes, ensuring reliability and scalability for both discovery scientists and AI agents.
Responsibilities:
• Build and maintain end-to-end ML deployment pipelines: experiment tracking, model versioning (MLflow, Weights & Biases), containerized model serving, and automated retraining triggers.
• Develop model registry infrastructure and feature engineering pipelines that enable computational scientists to access models.
• Implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems (LLMOps) to ensure system reliability and performance at scale.
• Build dashboards and metrics tracking for pipeline execution, API latency, token usage, model prediction quality, and system health
• Establish structured logging and tracing infrastructure for debugging and performance optimization across scientific data systems
• Deploy predictive and analytical methods from Methods4Insight (e.g. cheminformatics, structural biology, bioinformatics, reaction informatics) with versioning, structured error handling, and response-time guarantees that enable insight generation in agile manner. Productionize when and where needed in partnerships with Tech@Lilly.
• Build serving infrastructure supporting both synchronous (interactive scientist queries) and asynchronous (batch and agent-invoked) workloads in partnership with Tech@Lilly and Frontier AI.
• Define and implement API contracts, documentation standards, and testing frameworks that ensure scientific tools are analysis ready, robust and consumable by external teams including Frontier AI.
• Build and operate cloud-native model serving infrastructure (AWS, Azure, or GCP) using containers, Kubernetes, and infrastructure-as-code.
• Develop CI/CD pipelines for ML models: automated validation, A/B testing, canary deployments, and rollback procedures.
• Integrate model serving with Data Foundry’s data pipelines, ensuring models have access to properly formatted, versioned training and inference data.
• Partner with the Frontier AI team and Tech@Lilly to ensure Data Foundry’s scientific tools are exposed via well-defined interfaces (REST APIs, MCP-compatible endpoints) that agents can invoke programmatically.
• Collaborate on API performance requirements: latency targets, throughput guarantees, and graceful degradation under load.
• Work with Methods4Insight scientists to ensure deployed models include appropriate uncertainty quantification and confidence metrics.
Qualifications:
Required:
• B.S. or M.S. in Computer Science, Data Science, Machine Learning, Bioinformatics, Computational Biology, or related field.
• 3+ years of experience in MLOps, ML engineering, or scientific platform development.
• Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.
Preferred:
• Pharmaceutical or biotech research industry experience.
• Strong Python skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and ML lifecycle tools (MLflow, W&B, Kubeflow, or similar).
• Proven track record building and deploying production model serving infrastructure — containerized endpoints, RESTful/gRPC APIs, and operational monitoring.
• Working knowledge of cloud platforms (AWS, Azure, or GCP), Kubernetes, and CI/CD automation.
• Strong communication skills with ability to collaborate across computational scientists, software engineers, and partner teams.
• Experience operationalizing scientific or computational models (cheminformatics, bioinformatics, structural biology, QSAR, molecular simulations, PK/PD, systems biology, or ODE-based models).
• Hands-on experience with model monitoring, drift detection, and automated retraining systems.
• Familiarity with API gateway patterns, event-driven architectures, and service mesh technologies.
• Experience with feature stores, data versioning (DVC), or experiment tracking at scale.
• Exposure to AI agent frameworks (MCP, LangChain) or building APIs that AI systems invoke programmatically.
• Experience with C, C++, CUDA, or GPU-accelerated computing for optimizing model training/inference performance; familiarity with containerizing HPC workloads (Singularity/Apptainer).
Company:
We're a medicine company turning science into healing to make life better for people around the world. Founded in 1876, the company is headquartered in Indianapolis, USA, with a team of 10001+ employees. The company is currently Late Stage.