ML Ops Platform Engineer — Hybrid (Silicon Valley)

Eli Lilly and Company; San Francisco (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Eli Lilly and Company, in partnership with NVIDIA, is expanding its AI-driven drug discovery lab in Silicon Valley. We seek 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, collaborate with scientists and engineers, and drive production-grade AI capabilities across our discovery programs.

Qualifications

• Bachelor’s in Computer Science, Engineering, Statistics, Mathematics, or a related technical field.
• 5+ years of experience in machine learning engineering, ML Ops, or platform engineering.
• Strong Python skills and experience with ML frameworks such as PyTorch, JAX, or TensorFlow.
• Experience deploying, operating, and scaling production ML platforms, including model serving, monitoring, and large-scale inference workloads.

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

ML Ops

Team collaboration

Problem solving

Communication

Education

Bachelor's in CS/Engineering/Statistics/Math

Tools

MLflow

Weights & Biases

KServe

Docker

Kubernetes

Slurm

Ray

Terraform

Ansible

GitHub Actions

Job description

Eli Lilly and Company, in partnership with NVIDIA, is expanding its AI-driven drug discovery lab in Silicon Valley. We seek 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, collaborate with scientists and engineers, and drive production-grade AI capabilities across our discovery programs.