HPC AI/ML Platform Engineer

Description

We are 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.

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

Skills

Kubernetes, terraform, python, slurm, hpc, deep learning, Infrastructure

Top Skills Details

Kubernetes,terraform,python,slurm,hpc,deep learning

Experience Level

Expert Level

Job Type & Location

This is a Contract position based out of Seattle, WA.

Pay And Benefits

The pay range for this position is $100.00 - $100.00/hr.

Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.

Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following: Medical, dental & vision Critical Illness, Accident, and Hospital 401(k) Retirement Plan Pre-tax and Roth post-tax contributions available Life Insurance (Voluntary Life & AD&D for the employee and dependents) Short and long-term disability Health Spending Account (HSA) Transportation benefits Employee Assistance Program Time Off/Leave (PTO, Vacation or Sick Leave)

Job Type & Location

This is a Contract position based out of Seattle, WA.

Application Deadline

This position is anticipated to close on Oct 30, 2026.

Workplace Type

This is a fully remote position.

About TEKsystems

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