Senior ML Infrastructure Engineer - Large-Scale AI Platform

Jobgether • United States

Remote Visa Sponsorship

This Job is No Longer Active This position is no longer accepting applications

AI Summary

Design, build, and operate GPU clusters for large-scale ML training and inference. Collaborate with ML teams to optimize performance, cost, and developer experience. Ensure security and multi-tenant access control.

Key Highlights

Large-scale ML infrastructure

Cross-cloud, on-prem, hybrid environments

Collaboration with ML teams

Optimize performance, cost, and developer experience

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Key Responsibilities

Design, build, and operate GPU and accelerator infrastructure for large-scale training and inference workloads

Develop scheduling, queueing, and resource management systems

Integrate and support ML frameworks

Build and maintain high-performance storage and data pipelines

Design and optimize networking layers

Implement observability, monitoring, and failure analysis tools

Drive automation for provisioning, lifecycle management, and infrastructure configuration

Partner with ML teams to forecast capacity needs and improve developer workflows

Ensure security, isolation, and multi-tenant access control

Optimize cost efficiency across compute, storage, and networking

Technical Skills Required

Python Systems programming (Go or C++) Distributed systems

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Benefits & Perks

Competitive salary range: $100,000 - $150,000

100% remote (within the United States)

H1B transfer support for eligible candidates

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Nice to Have

Experience with RDMA/InfiniBand

FinOps for ML workloads

Open-source ML infrastructure

Job Description

This role focuses on building and operating the core platform that powers large-scale machine learning training and inference workloads.You will work on GPU cluster infrastructure spanning cloud, on-prem, and hybrid environments.The position plays a critical role in enabling efficient, reliable, and scalable AI development across multiple teams.You will design systems for scheduling, distributed training, storage throughput, and high-performance networking.The environment is highly technical, combining systems engineering, ML frameworks, and platform reliability at scale.You will collaborate closely with ML researchers and engineers to optimize performance, cost, and developer experience.

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