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EngineeringHybrid

Senior Staff Storage Engineer

DDN · Hybrid

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Last checked by MeritLog September 23, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from DDN's Ashby job board and last checked it on September 23, 2026.

Source: the employer's Ashby job board. Open the job post for the latest details.

Job details

Work model
Hybrid
Salary
Not listed by source
Location
Santa Clara Colocation

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How this role compares at DDN

DDN has 99 live roles in MeritLog’s job list across 10 job types, and 61 of them are in engineering. 33 of those jobs list a pay range. That is 33%.

These counts use the job boards we track and were checked when this page loaded. We compare only full pay ranges in the same currency and time period.

Job description

Role Overview We are seeking a Senior Staff Storage Backend Engineer to design and optimize the next generation of high-performance distributed storage systems. This role focuses on building the core I/O path, improving scalability, reliability, and software quality across large-scale storage clusters. The ideal candidate has deep expertise in C/C++, distributed systems, storage architecture, and performance optimization, with a proven track record of building highly available, low-latency infrastructure. Key Responsibilities Storage Architecture & Development - Design, develop, and optimize high-performance storage I/O paths for extreme throughput, low latency, and high concurrency. - Build and improve distributed storage components, including erasure coding, data protection, recovery, and data layout algorithms. - Develop scalable concurrency models, locking mechanisms, and fault-tolerant designs. Performance & Scalability - Analyze and optimize storage performance across CPU, memory, caching, scheduling, and data movement paths. - Drive profiling, benchmarking, and performance tuning across multi-node, multi-device environments. - Design asynchronous, event-driven architectures supporting AI workloads, high-speed data pipelines, and real-time analytics. Technical Leadership - Serve as a technical leader for the IO Path team, driving architecture decisions, design reviews, code reviews, and complex debugging efforts. - Partner with engineering leadership to define technical strategy, evaluate new technologies, and influence the product roadmap. - Mentor engineers and promote engineering excellence through strong engineering practices and rigorous technical reviews. Quality & Cross-Functional Collaboration - Collaborate with QE, storage, networking, performance, field, and support teams to deliver reliable enterprise-class software. - Partner with QE to identify quality gaps, improve validation strategies, and ensure robust coverage of complex distributed storage scenarios. - Debug complex customer and system issues and translate learnings into product improvements. - Influence CI/CD pipelines, automation frameworks, and release processes to enable scalable and reliable software delivery. Required Qualifications - 15+ years of experience developing large-scale systems software using C/C++. - 10+ years of hands-on experience designing and developing storage systems, distributed storage platforms, or high-performance I/O subsystems. - Deep understanding of storage architecture, I/O optimization, memory management, caching, scheduling, and data path design. - Experience with distributed storage concepts, including erasure coding, replication, recovery, and fault tolerance. - Hands-on experience with SPDK or similar high-performance user-space storage frameworks. - Strong knowledge of concurrency, synchronization, and distributed systems design. - Expertise in performance analysis, profiling, debugging, and system optimization. Preferred Qualifications - Experience with high-performance computing (HPC), AI infrastructure, or large-scale storage platforms. - Experience with distributed file systems, object storage, or hybrid storage architectures. - Knowledge of NVMe-oF, RDMA, and high-speed networking technologies. - Experience building observability, monitoring, and self-healing infrastructure. - Familiarity with containerized workloads and cluster orchestration.

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