Senior Software Engineer (Infrastructure Storage)

Lambda; United States; Hybrid; USD 150,000 - 230,000; Full time

Job summary

Lambda, The Superintelligence Cloud, seeks an experienced Senior Software Engineer to join the Storage team and help build next-generation on-premise storage software for AI workloads. You will shape storage software architecture, APIs, and resilience across file, block, and object storage.

You will collaborate with hardware teams to integrate NVMe and GPU-direct storage, tackle complex distributed systems challenges, and drive performance, reliability, and scalability across large data sets.

Qualifications

• Strong experience building scalable storage software.
• Deep knowledge of file, block, and object storage protocols.
• Experience with distributed systems.

Responsibilities

• Design, implement, and maintain storage software for on-premise solutions.
• Optimize APIs for file, block, and object access.
• Collaborate with hardware and system architects to integrate software with NVMe and GPU-direct storage.
• Troubleshoot production issues and ensure reliability.

Skills

Distributed systems

Storage protocols

Software engineering

Tools

NFS

SMB

iSCSI

Fibre Channel

S3

Job description

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU.If you'd like to build the world's best AI cloud, join us.

Note:

This position requires presence in our San Francisco, San Jose, or Bellevue office location 4 days per week; Lambda’s designated work-from-home day is currently Tuesday.

In the world of distributed AI training and inference, raw GPU and CPU horsepower is just a part of the story. High-performance networking and storage are the critical components that enable and unite these systems, making groundbreaking AI training and inference possible.

The Lambda Infrastructure Engineering organization forges the foundation of high-performance AI clusters by welding together the latest in AI storage, networking, GPU and CPU hardware.

Our expertise lies at the intersection of:

• High-Performance Distributed Storage Solutions and Protocols: We engineer the protocols and systems that serve massive datasets at the speeds demanded by modern clustered GPUs.
• Dynamic Networking: We design advanced networks that provide multi-tenant security and intelligent routing without compromising performance, using the latest in AI networking hardware.
• Compute Clustering and Virtualization: We enable cutting-edge virtualization and clustering that allows AI researchers and engineers to focus on AI workloads, not AI infrastructure, unleashing the full compute bandwidth of clustered GPUs.

AI training and inference relies on petabytes of data hosted on large, high-performance storage arrays. At Lambda, the Infrastructure Storage Team’s job is to ensure that the data powering AI is fast, performant, and available across a variety of access protocols (fit for purpose).

We're looking for an experienced Senior Software Engineer to join our storage team. You'll join a team responsible for developing and implementing storage software for our next-generation on-premise storage solutions. This role requires expertise in distributed systems, and an in-depth understanding of file, block, and object storage protocols. You'll work on building scalable and resilient storage services that power our AI and machine learning infrastructure.

What You’ll Do:

• Design, develop, and maintain software for storage systems, focusing on performance, scalability, and reliability.
• Implement and optimize storage protocol APIs for file (e.g., NFS, SMB), block (e.g., iSCSI, Fibre Channel), and object (e.g., S3) access.
• Develop distributed systems for managing and orchestrating storage resources across multiple storage solutions and redundant arrays.
• Collaborate with hardware and system architects to integrate software with various storage solutions, including NVMe and GPU-direct storage.
• Troubleshoot and debug complex issues in a production data c