Senior Software Engineer - Storage Control Plane

Lambda·San Francisco Office (Fremont St)·12 авг.

Senior Software Engineer - Storage Control Plane

🏢 ОфисSeniorПолная занятость

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Наша компания

Founded in 2012, with 500+ employees, and growing fast Our investors notably include TWG Global, US Innovative Technology Fund (USIT), Andra Capital, SGW, Andrej Karpathy, ARK Invest, Fincadia Advisors, G Squared, In-Q-Tel (IQT), KHK & Partners, NVIDIA, Pegatron, Supermicro, Wistron, Wiwynn, Gradient Ventures, Mercato Partners, SVB, 1517, and Crescent Cove We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG Our values are publicly available:

Чем предстоит заниматься

Design and build a vendor-agnostic control plane that provisions, scales, heals, and meters storage across the platforms our customers actually demand, VAST Data, WEKA, DDN, Pure, NetApp, Ceph, MinIO, and the ones that don't exist yet

Define the internal abstraction layer that hides vendor-specific APIs, failure semantics, QoS knobs, and telemetry formats behind one declarative interface, so a new vendor integration is a driver, not a re-architecture

Build reconciliation-loop and CRD-based orchestration (Kubernetes controllers, operators, custom schedulers) that manages capacity, tenancy, encryption domains, and placement across data centers and availability zones

Own multi-tenant isolation end to end: namespace and subsystem partitioning, per-tenant QoS and rate limiting, credential and key lifecycle, blast-radius containment, noisy-neighbor detection

Design the capacity and placement engine: PCIe-topology-aware, NUMA-aware, failure-domain-aware. On our platforms a drive behind the same PCIe switch as the GPU it serves beats a faster drive on a different root port, and the control plane needs to know that

Instrument everything: SLI/SLO definitions, fleet-wide performance regression detection, and the observability pipeline that makes a petabyte fleet debuggable at 3 a.m

Наши требования

Experience with AI/ML workloads and the unique storage challenges they present

Knowledge of data center networking and high-speed interconnects (e.g., InfiniBand, RoCE)

Experience with performance tuning and optimization of storage systems

Familiarity with hardware acceleration technologies, specifically GPUs and DPUs

Production experience with VAST Data, WEKA, DDN, Pure, NetApp, or IBM Storage Scale

Ceph at 100 PB+ in HPC or AI environments

CXL memory pooling, computational storage, ZNS SSDs, EDSFF

Published or presented at SNIA SDC, FAST, USENIX ATC, LSFMM+BPF, OCP, SC, or similar

Salary Range Information

Bachelor's or Master's degree in Computer Science or a related field

5+ years of experience in software development for storage systems

Proven experience with distributed systems programming and concepts such as load balancers, data-durability, consensus algorithms, fault tolerance, and data consistency

Strong programming skills in languages such as C, C++, Go, or Python

Experience with Linux kernel internals and system-level programming

Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar

Familiarity with containerization technologies like Docker and Kubernetes and running production workloads in these environments

Familiarity with CI/CD and QA practices for distributed systems development environments

Мы предлагаем

Health, dental, and vision coverage for you and your dependents

Wellness and commuter stipends for select roles

401k Plan with 2% company match (USA employees)

Flexible paid time off plan that we all actually use

Дополнительно

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 our next-generation storage software. 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 control plane that power our AI and machine learning infrastructure

The annual salary range for this position has been set based on market data and other factors. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description

Lambda

San Francisco Office (Fremont St)

ГрейдSenior

ЗанятостьПолная занятость

РегионНе Россия

ФорматОфис

ИсточникСкрыто

Опубликовано12 авг.

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