Neo Cloud - Principal AI Cloud Storage Engineer

Location: San Francisco, Seattle

Department: Data Center

Location Type: HYBRID

Employment Type: FULL_TIME

• 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.
• Deep understanding of data privacy and security and its implication on performance.
• Ability to partner with network engineers to deliver complete AI storage system.

• Strong bias to action and resolution of technical decisions. 
• 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.

• 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.

• 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.