> Markdown version of [/jobs/ext/550761-engineer-mlops-scientific-platforms-data-foundry](https://www.wearedevelopers.com/jobs/ext/550761-engineer-mlops-scientific-platforms-data-foundry). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Engineer - MLOps & Scientific Platforms - Data Foundry - **Company:** The Lilly Company - **Location:** San Diego, CA, United States - **Experience:** Experienced - **Salary:** $66,000.0 - $165,000.0 - **Contract:** Permanent contract - **Skills:** C (Programming Language), A/B Testing, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Application Performance Management, Microsoft Azure, Bioinformatics, C++ (Programming Language), Software Documentation, Computational Biology, Computer Simulation, Nvidia CUDA, Data Infrastructure, Data Systems, Software Debugging, Fault Tolerance, Monitoring of Systems, Python (Programming Language), Machine Learning, Molecular Modeling, Performance Tuning, Tensorflow, Management of Software Versions, Data Logging, High Performance Computing, Feature Engineering, Pytorch, Istio, Delivery Pipeline, Multi-Agent Systems, Reliability of Systems, Event Driven Architecture, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Virtual Agents, Api Design, Api Gateway, Restful APIs, Software Version Control, Data Pipelines, Api Management - **Published:** June 4, 2026 - **Apply:** https://dejobs.org/x/x/BA85075DE49B4CC6AED70587944EFFF2/job/ ## About the Role * 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 d evelopment * 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., * 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). ## Description We are seeking an Engineer - MLOps & Scientific Platforms - Data Foundry to operationalize Data Foundry's scientific tools and analytical methods into actionable-prototypes. You will build the ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails that make our scientific discovery methods and tools reliable, scalable, and consumable, both by discovery scientists and by the Frontier AI group's autonomous agents. This role sits at the interface between Methods4Insight (which develops analytical methods) and Architecture4Insight (which provides the agile data infrastructure). Your job is to ensure every scientific tool Data Foundry produces are analytics-ready, well-monitored, and exposed through APIs with the response-time guarantees and error handling that both human users and AI agents require. Responsibilities MLOps & Model Lifecycle Management * 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 Scientific Tool Agile Deployment * 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. Platform Engineering & Integration * 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. Frontier AI Interface & Collaboration * 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. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Rate-limiting using eBPF and Istio: How to protect your SaaS customers from themselves](https://www.wearedevelopers.com/videos/100220-rate-limiting-using-ebpf-and-istio-how-to-protect-your-saas-customers-from-themselves) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [From Model to Metal: An Open Source Stack for Accelerating Intelligence](https://www.wearedevelopers.com/videos/1636-from-model-to-metal-an-open-source-stack-for-accelerating-intelligence) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)