Senior HPC & AI Platform Engineer

Initial Therapeutics, Inc.; San Francisco (CA); On-site; USD 141,000 - 231,000; Full time

Benefits offered by this job

401(k)

Pension

Vacation benefits

Medical, dental, vision

Flexible benefits

Life insurance

Time off & leave

Well-being benefits

Job summary

Lilly seeks a skilled HPC Systems Administrator to build and operate scalable AI and high-performance computing platforms in a hybrid Silicon Valley setting. You’ll partner with AI scientists, engineers, and domain experts to enable efficient model training, inference, and experimentation across GPU, cloud, and on-premises environments.

Responsibilities include delivering highly available computing platforms, driving automation and standardization, and balancing reliability with cost efficiency

Qualifications

• Bachelor’s degree in CS, ECE, or related field.
• 5+ years deploying or supporting large-scale HPC/GPU/distributed computing environments.
• Experience with Slurm or Grid Engine for job scheduling.
• Proficiency in automation and container tech (Docker, Kubernetes, Ansible).
• Experience operating AI/HPC infra in cloud (AWS/Azure/GCP).

Responsibilities

• Build and operate scalable AI and HPC platforms for research and engineering.
• Collaborate with AI scientists, engineers, and domain experts to enable model training, inference and experimentation.
• Improve productivity, performance, and access to computing resources through automation.

Skills

Linux systems

Python

Ansible

Job scheduling (Slurm/Grid Engine)

Docker

Kubernetes

HPC/AI computing

Cloud platforms (AWS/Azure/GCP)

Networking & storage

Troubleshooting & observability

Education

Bachelor’s in CS/CE/Systems Eng or related

Tools

Slurm/Grid Engine

Docker

Kubernetes

Ansible

Job description

Lilly seeks a skilled HPC Systems Administrator to build and operate scalable AI and high-performance computing platforms in a hybrid Silicon Valley setting. You’ll partner with AI scientists, engineers, and domain experts to enable efficient model training, inference, and experimentation across GPU, cloud, and on-premises environments.

Responsibilities include delivering highly available computing platforms, driving automation and standardization, and balancing reliability with cost efficiency