ML Ops Engineer — Scalable AI for Drug Discovery (Hybrid)

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

Benefits offered by this job

401(k) plan

Pension

Medical, dental, vision benefits

Vacation benefits

Flexible benefits

Job summary

Eli Lilly and Company in Silicon Valley seeks an experienced ML Ops Engineer to build and operate platforms for end-to-end machine learning lifecycles. You enable reliable model deployment, monitoring, and reproducibility at scale, partnering with engineering and scientific teams to deliver production-ready AI capabilities.

The role emphasizes containerization, cloud/onsite GPU infrastructure, and automation, with a focus on performance and collaboration in a fast-paced environment.

Qualifications

• Bachelor’s in Computer Science, Engineering, Statistics, Mathematics, or a related technical field.
• 4+ years of experience in machine learning engineering, ML Ops, or platform engineering.
• Ability to work in a fast-paced environment with cross-functional teams.

Responsibilities

• Lead the operational lifecycle of ML models, including deployment, monitoring, and ongoing reliability.
• Operate large-scale inference platforms that support scientific discovery and AI workloads.
• Ensure models can be deployed, scaled, monitored, and maintained in production environments.
• Test, refine, and improve model accuracy.
• Work with data scientists, business analysts and partners to integrate ML models into broader strategies.
• Automate the platform with infrastructure-as-code and CI/CD, and document it well enough that someone else can operate it.

Skills

Python

ML frameworks

Model deployment

CI/CD automation

Docker/Kubernetes

Cloud platforms

Observability & monitoring

Cross-functional collaboration

Education

Bachelor's in Computer Science or related field

Tools

MLflow

Weights & Biases

KServe

Triton

Terraform

Ansible

GitHub Actions

Job description

Eli Lilly and Company in Silicon Valley seeks an experienced ML Ops Engineer to build and operate platforms for end-to-end machine learning lifecycles. You enable reliable model deployment, monitoring, and reproducibility at scale, partnering with engineering and scientific teams to deliver production-ready AI capabilities.

The role emphasizes containerization, cloud/onsite GPU infrastructure, and automation, with a focus on performance and collaboration in a fast-paced environment.