Senior ML Ops Engineer – Scale Production AI

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

Benefits offered by this job

Company bonus

401(k)

Medical insurance

Dental insurance

Vision insurance

Life insurance

Flexible benefits

Well-being programs

Job summary

Lilly is hiring an ML Ops Engineer for its Silicon Valley hub to build and run end-to-end ML platforms. You’ll enable reliable deployment, monitoring, retraining, and reproducibility at scale, collaborating with engineers and scientists to deliver production-ready AI capabilities.

You’ll optimize infrastructure, manage large-scale inference, and automate platforms with IaC and CI/CD, while ensuring observability and sound operational practices.

Qualifications

• BS in CS/Engineering/Stats or related field.
• 4+ years in ML engineering, ML Ops, or platform engineering.
• Strong Python and ML frameworks (PyTorch, JAX, TF).
• Experience deploying and scaling production ML platforms.
• Proficient in IaC and CI/CD practices.
• Experience with cloud (AWS/Azure/GCP) and on-prem GPUs.
• Observability, metrics, logging, and performance tuning.

Responsibilities

• Lead the ML lifecycle deployments, monitoring, and reliability.
• Operate large-scale inference platforms for research workloads.
• Ensure production deployment, scaling, and maintenance.
• Test and improve model accuracy and robustness.
• Collaborate with data scientists and infra teams to integrate models.
• Automate platform with IaC and CI/CD; document for ops.

Skills

Python

PyTorch

JAX

TensorFlow

ML frameworks

CI/CD

Docker

Kubernetes

Slurm

Ray

Terraform

Ansible

GitHub Actions

MLflow

Weights & Biases

KServe

AWS

Azure

GCP

Observability

Collaboration

Education

BS in Computer Science, Engineering, Statistics, Mathematics

4+ years ML engineering / ML Ops / platform engineering

Tools

MLflow

Weights & Biases

KServe

Docker

Kubernetes

Slurm

Ray

Job description

Lilly is hiring an ML Ops Engineer for its Silicon Valley hub to build and run end-to-end ML platforms. You’ll enable reliable deployment, monitoring, retraining, and reproducibility at scale, collaborating with engineers and scientists to deliver production-ready AI capabilities.

You’ll optimize infrastructure, manage large-scale inference, and automate platforms with IaC and CI/CD, while ensuring observability and sound operational practices.