AI & HPC Infrastructure Engineer

Franklin Fitch; United States; Hybrid; USD 120.000 - 180.000; Vollzeit

Benefits dieser Stelle

Remote-first model

Zusammenfassung

Franklin Fitch is partnering with a leading global research and advisory organization to scale its HPC and GPU capabilities in the United States. The role focuses on expanding GPU clusters from 8 to 32 NVIDIA H200s and shaping long-term AI infrastructure strategy.

Approximately 85% of time is engineering and platform development with some operational support. The ideal candidate will have 5–10 years in infrastructure, HPC or research computing, and proven project leadership with strong

Qualifikationen

• Experience in HPC or research computing environments.
• Strong GPFS / IBM Spectrum Scale experience.
• Proficient with job schedulers such as SLURM, LSF, or similar.
• Experience supporting distributed compute environments.
• Ability to lead projects independently with minimal oversight.
• Excellent troubleshooting across compute, storage, networking and hardware.
• Strong communication to explain complex topics to non-technical stakeholders.

Aufgaben

• Designing, deploying, and maintaining GPU-accelerated computing infrastructure.
• Supporting large-scale AI/ML and LLM training environments.
• Managing Linux-based HPC clusters and services.
• Administering and optimizing Spectrum Scale file systems.
• Managing NVIDIA GPU platforms and related tooling.
• Supporting resource scheduling through SLURM and related technologies.
• Performance tuning for distributed multi-GPU workloads.
• Building automation, monitoring, and operational tooling.
• Collaborating with researchers and data scientists to translate requirements into infrastructure.

Kenntnisse

HPC infrastructure

GPU infrastructure

Linux clustering

GPFS / Spectrum Scale

SLURM / LSF

Distributed compute

Kubernetes / containers

Automation / Ansible

Docker / Singularity

Ausbildung

Bachelor's degree in CS/EE/related field

Tools

GPFS

SLURM

LSF

NVIDIA GPUs

NVIDIA CUDA/cuDNN

Jobbeschreibung

Our client is a highly respected global consulting and research organization that supports leading commercial enterprises, public sector institutions, and professional services firms. As part of a significant investment in AI infrastructure, they are expanding their internal high performance computing (HPC) and GPU capabilities to support next-generation machine learning and large language model (LLM) initiatives.

This is an exciting opportunity to join a small, highly skilled infrastructure team at a pivotal stage of growth. The successful candidate will play a key role in scaling a GPU environment from 8 to 32 NVIDIA H200 GPUs while helping shape the organization's long-term AI and HPC strategy.

The role is heavily project-focused, with approximately 85% dedicated to engineering, architecture, and platform development activities, and a smaller proportion supporting operational needs.

The Opportunity

You'll work at the intersection of traditional HPC, research computing, and modern AI infrastructure, supporting both analytical workloads and large-scale model training environments.

Key responsibilities include:

• Designing, deploying, and maintaining GPU-accelerated computing infrastructure
• Supporting large-scale AI/ML and LLM training environments
• Managing Linux-based HPC clusters and associated services
• Administering and optimizing parallel file systems, particularly IBM Spectrum Scale (GPFS)
• Managing NVIDIA GPU platforms, CUDA, cuDNN, NCCL, and related tooling
• Supporting resource scheduling through SLURM and related technologies
• Performance tuning for distributed and multi-GPU workloads
• Building automation, monitoring, reporting, and operational tooling
• Collaborating with researchers, data scientists, and technical stakeholders to translate business requirements into infrastructure solutions
• Evaluating emerging AI infrastructure technologies and recommending future platform enhancements

Required Experience

We're particularly interested in candidates who combine deep infrastructure expertise with an understanding of how end-users consume HPC resources.

• Experience supporting HPC or research computing environments
• Strong experience with GPFS / IBM Spectrum Scale
• Familiarity with job schedulers such as SLURM, LSF, or similar
• Experience supporting distributed compute environments
• Ability to lead projects independently with minimal oversight
• Strong troubleshooting experience across compute, storage, networking, and hardware layers
• Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders

Highly Desirable

• Experience supporting AI/ML infrastructure or LLM platforms
• Kubernetes and container orchestration experience
• Experience tuning large model training and inference workloads
• Ansible
• Docker, Apptainer, or Singularity
• SAS platform experience alongside GPU/HPC expertise

Ideal Background

This role is particularly well suited to someone who:

• Has approximately 5-10 years of relevant infrastructure experience
• Is currently operating at a strong mid-level and ready for a senior step forward
• Can own and deliver significant technical projects independently
• Comes from an HPC, research computing, higher education, government, scientific computing, or technical enterprise environment
• Enjoys balancing infrastructure engineering with emerging AI technologies

Team & Culture

You'll join a close-knit team of experienced infrastructure specialists covering HPC operations, automation, applications, and platform engineering. The group operates in a highly collaborative remote-first model, with team members distributed across the United States.

A notable area of planned growth is AI and LLM infrastructure expertise, making this a high-visibility hire with significant opportunity for progression and influence.

If you're an HPC infrastructure engineer looking to move into a highly visible AI-focused environment while remaining close to the hardware, architecture, and platform engineering side of the stack, we'd love to hear from you.