ML Ops Engineer – Scale Production AI for Drug Discovery

100 Eli Lilly and Company; South San Francisco (CA); Hybrid; USD 147,000 - 268,000; Full time

Benefits offered by this job

Company-sponsored 401(k)

Pension

Vacation benefits

Medical, dental, vision benefits

Flexible benefits (FSA)

Life insurance

Job summary

Lilly in Silicon Valley seeks an ML Ops Engineer to build and operate end-to-end ML platforms, enabling reliable deployment, monitoring, and reproducibility at scale. You will optimize infrastructure for research workloads and collaborate with engineering and scientific teams to deliver production-ready AI capabilities.

The role offers a flexible hybrid model (three days onsite, two days remote) at our Silicon Valley hub, with a strong emphasis on CI/CD, automation, and scalable ML workflows.

Qualifications

• Lead deployment, monitoring and reliability of ML models.
• Deploy and scale production ML platforms and model serving.
• Collaborate with researchers and engineers in a fast-paced setting.

Responsibilities

• Operate end-to-end ML lifecycle platforms and CI/CD pipelines.
• Optimize GPU resources and infrastructure for discovery workloads.
• Collaborate with data scientists and AI engineers on productionization.

Skills

Python

Model deployment

MLOps tools

Docker

Kubernetes

Slurm

Ray

Triton

Terraform

CI/CD

GitHub Actions

AWS

GCP

Azure

Monitoring

Troubleshooting

Cross-functional teamwork

Education

Bachelor's degree in CS/Engineering/Statistics/Math

Tools

MLflow

Weights & Biases

KServe

Job description

Lilly in Silicon Valley seeks an ML Ops Engineer to build and operate end-to-end ML platforms, enabling reliable deployment, monitoring, and reproducibility at scale. You will optimize infrastructure for research workloads and collaborate with engineering and scientific teams to deliver production-ready AI capabilities.

The role offers a flexible hybrid model (three days onsite, two days remote) at our Silicon Valley hub, with a strong emphasis on CI/CD, automation, and scalable ML workflows.