Platform Engineer

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

Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley!

Making medicine that’s never been made means doing what’s never been done. If you’re an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation; we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us!

About the Lilly and NVIDIA Partnership

Lilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery’s toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe.

What You’ll Be Doing

As an ML Ops Engineer, you build and operate the platforms that run the end-to-end machine learning lifecycle. You enable reliable model deployment, operation, monitoring, retraining, and reproducibility at scale. You optimize infrastructure and GPU resources to support research and discovery workloads. You will work closely with engineering and scientific teams to deliver production-ready AI capabilities.

How You’ll Succeed

• 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.

What You Should Bring

• Strong Python skills and experience working with machine learning frameworks such as PyTorch, JAX, or TensorFlow.
• Experience deploying, operating, and scaling production machine learning platforms, including model serving, monitoring, and large-scale inference workloads.
• Experience with MLOps platforms and tools like MLflow, Weights & Biases, KServe, or similar technologies.
• Proficiency with containerization, orchestration, and distributed compute environments (Docker, Kubernetes, Slurm, Ray).
• Experience operating large-scale AI platforms that deploy, host, and optimize machine learning models for production use, using technologies such as Triton, vLLM, or TensorRT-LLM.
• Experience with infrastructure automation and CI/CD practices using tools such as Terraform, Ansible, GitHub Actions, or related.
• Experience supporting cloud platforms (AWS, Azure, or GCP) and on-premises GPU infrastructure.
• Knowledge of observability and operational monitoring, including metrics, logging, tracing, and performance tuning.
• Ability to identify and address system, infrastructure, and model performance issues through automation and continuous improvement.
• Ability to collaborate effectively with research scientists, AI engineers, and infrastructure teams in a fast-paced environment.

Your Basic 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.

Location & Work Flexibility

This role is based at our Silicon Valley Hub. We offer a flexible hybrid work model, with three days onsite and two days working remotely each week, supporting both collaboration and work‑life balance.