ML Ops Engineer: Scale Production AI for Drug Discovery

Eli Lilly and Company; Indianapolis (IN); Hybrid; USD 147,000 - 268,000; Full time

Benefits offered by this job

Company bonus

401(k) plan

Pension

Vacation benefits

Medical, dental, vision

Job summary

Eli Lilly and Company in Silicon Valley seeks an ML Ops Engineer to build and run the end-to-end ML lifecycle, deploying and monitoring models, optimizing GPU resources, and delivering production-ready AI capabilities.

You will collaborate with data scientists and infrastructure teams in a hybrid setup (three days onsite, two remote), with a compensation package including performance bonus and comprehensive benefits.

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

MLOps

Model serving

Monitoring

Inference workloads

MLflow

Weights & Biases

KServe

Docker

Kubernetes

Slurm

Ray

Triton

vLLM

TensorRT-LLM

Terraform

Ansible

GitHub Actions

AWS

Azure

GCP

Observability

Automation

CI/CD

Collaboration

Cross-functional teamwork

Education

Bachelor's in Computer Science, Engineering, Statistics, Mathematics, or related field

Tools

Docker

Kubernetes

Slurm

Ray

Job description

Eli Lilly and Company in Silicon Valley seeks an ML Ops Engineer to build and run the end-to-end ML lifecycle, deploying and monitoring models, optimizing GPU resources, and delivering production-ready AI capabilities.

You will collaborate with data scientists and infrastructure teams in a hybrid setup (three days onsite, two remote), with a compensation package including performance bonus and comprehensive benefits.