Neo Cloud - Principal AI Cloud Storage Engineer

Blaze Talent•Seattle, WA

13h•Remote

About The Position

Neo Cloud is seeking a Principal Software Engineer to define and build its AI cloud storage platform. This is a senior individual-contributor role for an experienced system architect who can operate at the intersection of customer needs, system architecture, and production operations. The role involves translating future AI/ML customer storage needs into a performant, resilient, and economical storage system at massive scale.

Requirements

10+ years of professional software engineering experience building and operating cloud storage systems in production.
Direct experience with AI-focused storage platforms such as DDN, Weka, or VAST, including their architectural approaches to throughput, caching, and GPU-cluster integration.
Deep understanding of distributed systems fundamentals: consistency models, replication, consensus, partitioning, failure detection, and recovery.
Proven experience operating high-scale distributed systems in production, including on-call ownership, incident response, and driving systemic reliability improvements.
Strong systems programming skills (e.g., Go, C++, Rust, or Java) and comfort working across the stack from low-level I/O and networking to distributed control planes.
Excellent written and verbal communication skills.

Nice To Haves

Experience designing or tuning local node caching layers to accelerate AI training and inference data access.
Experience with high-performance networking.
Contributions to open-source storage projects, relevant patents, or published technical papers/talks.

Responsibilities

Engage directly with customers, solutions architects, and product teams to understand future storage requirements for AI/ML workloads.
Extrapolate from current usage patterns and industry trends to anticipate future requirements.
Partner with product management to prioritize platform investments based on near-term customer needs and longer-term strategic bets.
Design, implement, and operate AI cloud object and file storage systems, including the data path, metadata path, and control plane.
Take a system-level approach that accounts for the full characteristics of AI workloads, building end-to-end solutions — including local node caching strategies, the network hardware and protocols that move data between storage and compute (e.g., RDMA, high-throughput NICs, congestion control), and the underlying storage hardware and software (media, erasure coding, metadata services) — and understanding how decisions in one layer constrain or unlock the others.
Architect for the specific demands of AI workloads: very high aggregate throughput to keep GPU/accelerator clusters fed, support for massive numbers of small and large objects and files, efficient checkpointing at scale, and predictable tail latency under heavy concurrent load.
Drive core storage system design decisions, including durability and consistency models, erasure coding and replication strategies, metadata scalability, multi-tenancy and isolation, and S3-compatible and POSIX/file-protocol API design.
Take end-to-end ownership of services in production: build for observability and operability from day one, participate in on-call, lead incident response and root-cause analysis for critical issues, and drive long-term reliability and performance improvements.
Identify and eliminate performance bottlenecks and scalability limits before they become customer-facing problems; lead capacity planning for rapid growth.
Ensure deep understanding of data privacy and security and its implication on performance.
Partner with network engineers to deliver complete AI storage system.
Set technical direction and best practices for the storage organization; author and review design documents for significant architectural changes.
Provide deep technical mentorship to senior and staff engineers; raise the engineering bar across the team through code review, design review, and hands-on collaboration.
Influence technical strategy across adjacent teams.