Intelligent Infrastructure Engineer

Role Description

We are seeking an Intelligent Infrastructure Engineer to design, build, and operate the platform layer that powers large-scale AI training and inference workloads. The role focuses on:

• GPU clusters
• Distributed training frameworks
• Scheduling
• Storage performance
• Developer experience for ML engineers and researchers

There is a strong emphasis on reliability, efficiency, and cost control. The ideal candidate has built or operated production AI infrastructure at scale, understands the interaction between hardware, kernel, scheduler, and ML framework, and brings strong software engineering discipline to platform work.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related field.
• Six or more years of experience in infrastructure, platform, or HPC engineering.
• Hands-on experience operating GPU clusters or large-scale ML training infrastructure.
• Strong proficiency in Python and at least one systems language such as Go or C++.
• Deep understanding of distributed training, accelerator architectures, and collective communication.
• Experience with Kubernetes, Slurm, Ray, or similar scheduling systems for ML workloads.
• Strong understanding of Linux internals, networking, and high-performance storage.
• Experience with at least one major cloud provider’s ML infrastructure offerings.
• Strong software engineering practices including testing, CI/CD, and code review.
• Excellent communication and cross-functional collaboration skills.

Requirements

• Design and operate GPU and accelerator infrastructure for training and inference, spanning on-prem clusters, cloud-managed services, and hybrid configurations.
• Build scheduling, queueing, and resource-sharing systems that maximize accelerator utilization across many teams.
• Integrate frameworks such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a unified platform offering.
• Operate high-performance storage systems and data pipelines that keep accelerators fed with training data at near-line-rate.
• Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth collective communication.
• Build observability for AI workloads including utilization, throughput, training stability, and failure-mode analytics.
• Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs at scale.
• Drive cost optimization across compute, storage, and networking through scheduling, spot capacity, and right-sizing.
• Develop developer tooling and paved-road workflows that let researchers launch experiments safely and efficiently.
• Partner with research and applied ML teams to plan capacity for upcoming training runs.
• Implement security controls, isolation, and access management for multi-tenant AI infrastructure.
• Drive automation across cluster provisioning, lifecycle management, and configuration enforcement.
• Maintain runbooks, capacity dashboards, and operational documentation for the AI platform.
• Stay current with AI infrastructure research, accelerator hardware, and emerging open-source AI tooling.

Benefits

• Competitive salary range of $100,000–$150,000 Annually.
• 100% Remote work opportunity.
• Tremendous career growth potential.