Contract W2: HPC AI/ML Platform Engineer- Remote

HPC AI/ML Platform Engineer 12+ month contract 100% remote USC/GC Top Skills’ Details

• HPC / AI Infrastructure Experience (Must Have): Hands-on experience supporting High Performance Computing (HPC), AI/ML, Deep Learning, or GPU-intensive production environments at enterprise scale. Candidates should demonstrate experience supporting model training workloads, distributed compute platforms, or large-scale AI infrastructure.
• Kubernetes at Scale: Strong experience operating production Kubernetes platforms supporting high-performance workloads, including cluster lifecycle management, scheduling, networking, upgrades, troubleshooting, and platform reliability.
• Slurm + AI Compute Operations: Experience supporting Slurm or similar workload schedulers used to allocate compute resources across AI model training and inference workloads. Understanding of GPU utilization, workload orchestration, and distributed compute environments is highly desired.
• Infrastructure as Code & Automation: Strong Terraform and Python experience for provisioning, managing, automating, and scaling infrastructure environments.

Job Description Client is seeking experienced HPC AI/ML Platform Engineers to support one of the world’s largest AI compute environments. This team operates and scales multi-cloud infrastructure powering GPU-intensive model training, deep learning research, and production AI workloads across AWS, CoreWeave, GCP, OCI, and other emerging compute providers.

This is not a traditional cloud infrastructure role. The ideal candidate has hands-on experience supporting High-Performance Computing (HPC), deep learning platforms, large-scale GPU environments, and AI/ML training infrastructure where performance, reliability, and efficient resource utilization are mission critical.

Engineers on this team will help provision and operate large-scale compute environments, manage Kubernetes-based infrastructure, optimize GPU resource allocation, support Slurm scheduling systems, and build automation that enables rapid AI platform growth.

Success in this role requires experience operating production environments that support AI model development, training, and inference workloads at significant scale.

Must Have Requirements Proven experience supporting High Performance Computing (HPC) environments at enterprise scale

Experience supporting AI/ML or Deep Learning infrastructure, including model training workloads

Provision HPC infrastructure through CI/CD systems across AWS, CoreWeave, GCP, OCI, and additional cloud providers

Experience managing or supporting Slurm-based job scheduling environments

Operate and troubleshoot large-scale Kubernetes platforms supporting GPU-intensive workloads

Define and maintain SLIs/SLOs, monitoring, alerting, incident response, and post-incident reviews

Partner closely with Networking, Storage, Security, and AI/ML Platform teams

Experience operating environments where performance, cluster stability, and GPU utilization directly impact business outcomes

Requirements 4+ years supporting HPC, AI/ML Infrastructure, Machine Learning Platforms, or Deep Learning environments

Strong experience with distributed compute, GPU infrastructure, or large-scale training environments

Hands-on Kubernetes Experience At Scale Including Cluster lifecycle management

Scheduler troubleshooting

Node pool management

Networking and CNI troubleshooting

Production upgrades across large fleets

Terraform experience building and managing infrastructure through Infrastructure as Code

Python experience for platform automation and operational tooling

Working knowledge of AWS services including EC2, S3, EFS, and FSx for Lustre

Experience With Slurm Job Scheduling Strongly Preferred Experience supporting ML training, inference, deep learning, large language model (LLM), or GPU-intensive workloads preferred

Business Drivers/Customer Impact The client’s AI infrastructure footprint is growing faster than operational capacity. As GPU clusters continue to scale, the organization requires engineers with deep HPC and AI infrastructure expertise to ensure efficient resource utilization, platform reliability, and uninterrupted model training operations. Failures within these environments can result in significant GPU underutilization, degraded research velocity, and incre…