Lightning AI · London, England, United Kingdom; New York, New York, United States; Remote; San Francisco, California, United States; Seattle, Washington, United States

Infrastructure Engineer (Storage)

最近核查時仍有刊登最近確認刊登:2026-11-05 18:37:37.559209+00

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經驗5+ years of experience in infrastructure engineering, systems engineering, or related roles

來源發布日期:2026-05-01T15:18:58-04:00

原文資格條件

以下摘錄自招聘來源,適用條件請以原文為準。

  • This role may be fully remote within the U.S., or hybrid out of one of our office hubs (NYC, SF, Seattle, or London), with occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.
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原始資料於 2026-11-04T15:07:56.209314+00:00 查閱;內容可能已更新,請查看原始職位。

What We're Looking For
Lightning AI is seeking an experienced Senior Storage Infrastructure Engineer to join our Infrastructure Engineering team.
In this role, you will focus on building and operating the storage systems that power large-scale AI/ML training, inference, and HPC workloads. You will work at the intersection of software, hardware, and operations—developing automation, improving reliability, and scaling distributed storage systems across our bare-metal infrastructure.
You will help own the data plane of our storage infrastructure, supporting high-throughput, low-latency data access for some of the most demanding AI workloads. You’ll play a key role in managing and evolving our storage stack (including VAST and S3-compatible systems like Ceph), ensuring performance, reliability, and efficiency at scale.
This role may be fully remote within the U.S., or hybrid out of one of our office hubs (NYC, SF, Seattle, or London), with occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.
What You'll Do
Storage Systems & Infrastructure
Operate and scale distributed storage systems, including VAST and S3-compatible object storage (e.g., Ceph)
Improve performance, reliability, and efficiency of storage systems supporting large-scale AI/ML workloads
Troubleshoot complex storage and data path issues across hardware and software layers
Optimize storage performance to support high-throughput, low-latency AI training and inference workloads
Automation & Tooling
Build and maintain automation for provisioning, managing, and monitoring storage infrastructure
Develop Python-based tools and workflows to reduce manual operational overhead
Improve lifecycle management of storage clusters, from deployment through maintenance and scaling
Systems & Operations
Manage and operate Linux-based systems in production, including bare-metal environments
Partner with infrastructure and data center teams on hardware bring-up, upgrades, and issue resolution
Support capacity planning, utilization tracking, and forecasting for storage systems
Leverage monitoring and telemetry to diagnose issues and improve system performance and reliability
Cross-Functional Collaboration
Work closely with Infrastructure Engineering, Network Engineering, and Platform teams to integrate storage into the broader platform
Contribute to design discussions around new infrastructure deployments and scaling strategies
Help define best practices for operating storage systems in high-performance computing environments
What You'll Need
Required Qualifications
5+ years of experience in infrastructure engineering, systems engineering, or related roles
Hands-on experience operating distributed storage systems (e.g., VAST, Ceph, or similar)
Strong Linux systems experience in production environments
Proficiency in Python or similar scripting/programming languages for automation
Experience working with bare-metal infrastructure and hardware-oriented systems
Ability to debug complex issues across system boundaries (storage, OS, hardware, networking)
Experience with storage networking protocols (e.g., NFS or similar)
Experience with capacity planning, monitoring, and performance tuning
Ideal Experience
Experience with VAST storage systems in production environments
Experience operating S3-compatible object storage at scale
Data center operations experience, including working with physical hardware
Familiarity with AI/ML or HPC workloads and their storage requirements
Background in high-performance or low-latency distributed systems
Familiarity with high-performance data transfer technologies (e.g., RDMA, GPU Direct Storage)
Experience supporting GPU-based workloads or large-scale compute clusters

來源:公司官网 · 資料查閱日期 2026-11-04T15:07:56.209314+00:00。職位狀態及要求請以原始招聘頁面為準。