Senior Storage Software Engineer, DGXC Data Services
$152,000–$241,500 year
RemoteUnited States or California, United States
Job Summary
Build storage technologies, client libraries, and filesystem frameworks enabling AI workloads to access data across object stores, file systems, and hybrid cloud infrastructure. Develop high-performance storage paths for training and inference workflows, including data loading, checkpointing, caching, POSIX-style access, and object-store integration. Construct observability systems that diagnose storage bottlenecks, attribute GPU idle time to I/O behavior, and expose actionable telemetry through production monitoring stacks. Improve performance, scalability, and reliability of storage systems serving massive datasets, deep directory trees, and high-concurrency AI workloads. Collaborate with internal AI teams, platform teams, SRE, and operations to validate storage behavior against real workloads and production environments using modern software engineering practices.
Required Qualifications
- BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience
- 5+ years of software engineering experience
- Strong foundation in algorithms, data structures, distributed systems, operating systems, and practical software design
- Experience building performance-sensitive systems, storage, backend, or cloud-native software in languages such as Go, Python, Rust, C/C++, or Java
- Experience with storage systems, object stores, caching, Linux systems, Kubernetes, or cloud infrastructure
- Ability to reason about performance, scalability, concurrency, reliability, and operational tradeoffs in production systems
- Ability to design APIs, document systems, communicate clearly, and break ambiguous infrastructure problems into practical execution plans
- Curiosity and practical judgment around AI-assisted or agentic engineering workflows, including using clear intent, specifications, acceptance criteria, tests, and verification to guide development
Desired Qualifications
- Background with Linux kernel observability, eBPF, tracing, or low-overhead telemetry systems
- Experience with FUSE, POSIX filesystems, object-store-backed filesystems, or filesystem metadata/indexing
- Experience optimizing storage performance for AI training, checkpointing, inference, or large-scale data pipelines
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