ML Infrastructure Engineer

Job summary

Seeking an AI Infrastructure Engineer to design, build, and operate the platform layer for large-scale AI training and inference workloads, focusing on GPU clusters and developer experience.

Qualifications

• Six or more years of experience in infrastructure, platform, or HPC engineering.
• Hands-on experience operating GPU clusters or large-scale ML training infrastructure.
• Experience with major cloud provider’s ML infrastructure offerings.

Responsibilities

• Design and operate GPU and accelerator infrastructure for training and inference.
• Build scheduling, queueing, and resource-sharing systems for accelerator utilization.
• Integrate frameworks into a unified platform offering.
• Operate high-performance storage systems and data pipelines.
• Design networking architectures supporting RDMA and high-bandwidth communication.
• Build observability for AI workloads.
• Implement checkpointing and fault-tolerance patterns for training jobs.
• Drive cost optimization across compute, storage, and networking.
• Develop tooling and workflows for researchers.
• Partner with ML teams to plan capacity for training runs.
• Implement security controls and access management.
• Drive automation across cluster provisioning and lifecycle management.
• Maintain operational documentation for the AI platform.
• Stay current with AI infrastructure research and emerging tools.

Skills

Degrees

Relocation