HPC Systems Administrator

Scorpion Therapeutics; South San Francisco (CA); Hybrid; USD 140,000 - 200,000; Full time

Job summary

Lilly is seeking an HPC Systems Administrator to design and maintain scalable HPC environments supporting AI-driven drug discovery. You will manage GPU-enabled systems, automate operations, and optimize infrastructure across on-prem and cloud resources to enable robust model training and experimentation.

The role combines Linux engineering, scripting, and HPC scheduling, with a focus on secure, high-availability platforms in a Silicon Valley hybrid setup (onsite 3 days, remote 2 days).

Qualifications

• Bachelor’s in Computer Science or related field.
• 5+ years supporting large-scale HPC or GPU compute environments.
• Expertise in Linux administration, scripting (Python, Bash), and automation tools (Ansible, Kubernetes).
• Experience with HPC job schedulers (Slurm, Grid Engine).
• Preferred: NVIDIA GPU infrastructure and cloud platforms; regulated/critical environments.

Responsibilities

• Deploy secure, high-availability AI and HPC systems.
• Automate operations for efficiency and reliability.
• Optimize infrastructure for performance, reliability, and cost-effectiveness.
• Collaborate with scientists and engineers to drive model training and experimentation across GPU, cloud, and on-premises environments.

Skills

Linux administration

Python

Bash scripting

Automation tooling

HPC scheduling (Slurm Grid Engine)

GPU compute experience

Cloud platforms (AWS Azure GCP)

Education

Bachelor's degree in Computer Science or related field

Tools

Ansible

Kubernetes

Slurm

Grid Engine

NVIDIA GPU infrastructure

AWS

Azure

GCP

Job description

Role: HPC Systems Administrator supporting Lilly's AI-driven drug discovery partnership with NVIDIA, focused on building and maintaining scalable high-performance computing platforms for advanced machine learning workloads.

Responsibilities include deploying secure, high-availability AI and HPC systems; automating operations for efficiency; optimizing infrastructure for performance, reliability, and cost-effectiveness; and collaborating with scientists and engineers to facilitate model training and experimentation across GPU, cloud, and on-premises environments.

Requirements: Bachelor’s in Computer Science or related field; 5+ years supporting large-scale HPC or GPU compute environments; expertise in Linux administration, scripting (Python, Bash), automation tools (Ansible, Kubernetes), and HPC job schedulers (Slurm, Grid Engine). Preferred skills include experience with NVIDIA GPU infrastructure, cloud platforms (AWS, Azure, GCP), and working in regulated or critical environments. The role is based in Silicon Valley with a hybrid work model (3 days onsite, 2 remote).

High-value specifics

Focus on AI/ML workloads, GPU hardware management, distributed computing, and automation for scientific research in drug discovery.