Engineer – MLOps, Scientific Platforms

Eli Lilly and Company

Engineer role in MLOps & Scientific Platforms at Lilly supporting AI-native drug discovery. Building ML deployment pipelines and collaborating with interdisciplinary teams.

Posted 5/28/2026full-timeSan Francisco • California, Colorado, Massachusetts • 🇺🇸 United StatesMid-LevelSenior💰 $66,000 - $165,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformGRPCKubernetesPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Operationalize Data Foundry’s scientific tools and analytical methods into actionable prototypes
Build the ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails
Ensure every scientific tool Data Foundry produces are analytics-ready, well-monitored, and exposed through APIs
Maintain end-to-end ML deployment pipelines: experiment tracking, model versioning, containerized model serving
Develop model registry infrastructure and feature engineering pipelines
Implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems to ensure system reliability and performance at scale
Productionize predictive and analytical methods from Methods4Insight with versioning and structured error handling
Build serving infrastructure supporting both synchronous and asynchronous workloads
Define and implement API contracts, documentation standards, and testing frameworks
Build and operate cloud-native model serving infrastructure using containers, Kubernetes, and infrastructure-as-code
Develop CI/CD pipelines for ML models and integrate model serving with Data Foundry’s data pipelines
Collaborate with the Frontier AI team and Tech@Lilly to ensure Data Foundry’s scientific tools are exposed via well-defined interfaces

Requirements

What you’ll need

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
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)

Benefits

Comp & perks

eligibility to participate in a company-sponsored 401(k)
pension
vacation benefits
eligibility for medical, dental, vision and prescription drug benefits
flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts)
life insurance and death benefits
certain time off and leave of absence benefits
well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonMLOpsML engineeringML frameworksPyTorchTensorFlowscikit-learnCI/CD automationmodel monitoringGPU-accelerated computing

Soft Skills

strong communication skillscollaboration