Staff Storage Engineer

Lambda•California, PA

9d•Onsite

About The Position

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. Product Engineering at Lambda is responsible for building and scaling our cloud offering. Our scope includes the Lambda website, cloud APIs and systems as well as internal tooling for system deployment, management and maintenance. For distributed AI workloads, GPU compute power is only one factor. High-performance networking and storage are essential for interconnecting these systems and supporting AI training and inference at scale. Lambda’s Infrastructure Engineering team integrates advanced storage, networking, and compute hardware to build high-performance clusters. Our expertise lies at the intersection of: High-Performance Distributed Storage Solutions: We deploy and maintain the storage systems that provide customer training and inference datasets at the speeds demanded by modern clustered GPUs. Software Defined Networking: We deploy software defined network overlays that provide multi-tenant security and intelligent routing without compromising performance, using the latest in high-performance networking hardware. Compute Virtualization: We enable virtualization that allows AI researchers and engineers to focus on AI workloads, not AI infrastructure. Cluster Integrity: We own the cluster integrity lifecycle: validating deployments, diagnosing performance and health across hardware and fabrics, and providing proactive remediation. About the Role: You will focus on strategy, architecture, and organizational influence Strategic Selection: Lead the RFP process and drive evidence-based storage solution selection and vendor evaluations. Workload Optimization: Develop an in-depth understanding of AI/ML workload profiles to influence future storage architecture and performance tuning. Operational Strategy: Identify and lead high-impact operational improvements and cross-functional deployment plans. Customer Discovery: Partner with leadership during deal formation to gather technical requirements and inform solution design. Organizational Leadership: Delegate complex engineering tasks and maintain consistent, proactive communication with the engineering leadership team.

Requirements

8+ years of experience designing, building, and operating large-scale multi-petabyte storage production environments
A strong understanding of diverse storage solutions and their ecosystems:
Familiarity with one or more storage solutions of the following vendors: Vast, Weka, DDN, NetApp, PureStorage, Dell, IBM, HPE
File, Block, and Object storage types
Storage Network Access Protocols such as NFS, SMB, and POSIX-compliant protocols.
NVMEoverFabricStorage Transport Protocols: NVME/TCP, NVME/IB, or NVME/RoCE
Storage performance via RDMA, GPUDirect Storage, parallel file systems
Encryption, storage security, and multi-tenancy strategies
Storage data-reduction, compression, and encryption
Backup and data protection
5+ years of experience in Infrastructure as Code (e.g. Terraform, Ansible).

Nice To Haves

Experience with Kubernetes, including CSI and COSI drivers and CNI’s
Deep understanding of storage performance
Strong understanding of public cloud features (e.g., SDN, block storage, distributed file systems, identity management)
Experience deploying, operating, and maintaining Software Defined Storage
Have implemented either open-source or commercial monitoring solutions of storage and storage-adjacent solutions

Responsibilities

Lead the RFP process and drive evidence-based storage solution selection and vendor evaluations.
Develop an in-depth understanding of AI/ML workload profiles to influence future storage architecture and performance tuning.
Identify and lead high-impact operational improvements and cross-functional deployment plans.
Partner with leadership during deal formation to gather technical requirements and inform solution design.
Delegate complex engineering tasks and maintain consistent, proactive communication with the engineering leadership team.