Senior Software Engineer - Storage Control Plane Software

Senior Software Engineer - Storage Control Plane Design and implement a vendor-agnostic storage control plane for AI workloads Location: San Francisco, California; Bellevue, Washington; San Jose, California Compensation: $266,000 - 395,000 USD / year About The Role Senior Software Engineer 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. 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 Dynamic Networking Compute Clustering and Virtualization 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. What You'll Do 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. You 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. Nice to Have 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 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. About Lambda 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: We offer generous cash & equity compensation 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 Equal Opportunity Employer Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law. #J-18808-Ljbffr Front Door Defense