ML Ops Engineer - Hybrid (Silicon Valley AI Lab)

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

Benefits offered by this job

401(k)

Pension

Vacation benefits

Medical, dental, vision benefits

Flexible benefits

Life insurance

Job summary

Eli Lilly and Company in Silicon Valley seeks an ML Ops Engineer to build and operate the end-to-end machine learning lifecycle, enabling reliable deployment, monitoring, and reproducibility at scale.

You will collaborate with researchers, AI engineers, and infrastructure teams to optimize GPU resources, automate CI/CD workflows, and deliver production-ready AI capabilities in a hybrid, three-days-onsite, two-days-remote model.

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.

Responsibilities

• Lead the operational lifecycle of ML models, including deployment, monitoring, and ongoing reliability.
• Operate and optimize 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

PyTorch

JAX

TensorFlow

Model deployment

Docker

Kubernetes

Terraform

GitHub Actions

Cloud platforms (AWS/Azure/GCP)

Education

Bachelor’s in Computer Science, Engineering, Statistics, Mathematics, or a related technical field

Tools

MLflow

Weights & Biases

KServe

Triton

TensorRT-LLM

Slurm

Ray

Job description

Eli Lilly and Company in Silicon Valley seeks an ML Ops Engineer to build and operate the end-to-end machine learning lifecycle, enabling reliable deployment, monitoring, and reproducibility at scale.

You will collaborate with researchers, AI engineers, and infrastructure teams to optimize GPU resources, automate CI/CD workflows, and deliver production-ready AI capabilities in a hybrid, three-days-onsite, two-days-remote model.