{"id":2504138,"url":"https://alion.io/job/blaze-talent-neo-cloud-principal-ai-cloud-storage-engineer","title":"Neo Cloud - Principal AI Cloud Storage Engineer","company":{"id":4421824,"name":"Blaze Talent","domain":"blazetalent.com","url":"https://alion.io/company/blazetalent","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Gem","truth_index":{"grade":"C","score":66,"open_postings":39,"ghost_share":0.564,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":203000,"max_usd":420000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":782},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon S3","optional":false},{"name":"C++","optional":false},{"name":"CoreWeave","optional":false},{"name":"Digital Twin","optional":false},{"name":"Java","optional":false},{"name":"Rust","optional":false}],"status":"live","first_seen_at":"2026-10-06T13:37:07Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-28T02:43:30Z","board_verified":true,"closed_at":null,"days_open":21,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":20},"description":"Member of Technical Staff, AI Cloud Storage\nLocation: Bay Area/ Seattle/ Remote\nReports to: CTO\nAbout Neo Cloud\nNeo Cloud is a fast growing next-generation AI neocloud, founded by ex NVIDIA, CoreWeave and Intel engineering leaders. We offer you the opportunity to build and operate next generation AI infrastructure that powers the world's most demanding AI workloads for the largest AI labs. We are rethinking how to run large scale AI infra for instance by leveraging agentic AI to build digital twins to de-risk our physical deployments. Our agents are deploying and monitoring our fleet to maximize the uptime of our infra.\nAbout the Role\nWe're looking for a Principal Software Engineer to define and build Neo Cloud's 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 - translating what AI/ML customers will need in the future, into a storage system that is performant, resilient, and economical at massive scale.\nKey Responsibilities\nIdentifying customer requirements:\nEngage directly with customers, solutions architects, and product teams to understand future storage requirements for AI/ML workloads.\nExtrapolate from current usage patterns and industry trends to anticipate future requirements.\nPartner with product management to prioritize platform investments based on near-term customer needs and longer-term strategic bets.\nSystem design, implementation, and operations:\nDesign, implement, and operate AI cloud object and file storage systems, including the data path, metadata path, and control plane.\nTake 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.\nArchitect 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.\nDrive 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.\nTake 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.\nIdentify and eliminate performance bottlenecks and scalability limits before they become customer-facing problems; lead capacity planning for rapid growth.\nDeep understanding of data privacy and security and its implication on performance.\nAbility to partner with network engineers to deliver complete AI storage system.\nTechnical leadership:\nStrong bias to action and resolution of technical decisions. \nSet technical direction and best practices for the storage organization; author and review design documents for significant architectural changes.\nProvide deep technical mentorship to senior and staff engineers; raise the engineering bar across the team through code review, design review, and hands-on collaboration.\nInfluence technical strategy across adjacent teams.\nQualifications\n10+ years of professional software engineering experience building and operating cloud storage systems in production.\nDirect experience with AI-focused storage platforms such as DDN, Weka, or VAST, including their architectural approaches to throughput, caching, and GPU-cluster integration.\nDeep understanding of distributed systems fundamentals: consistency models, replication, consensus, partitioning, failure detection, and recovery.\nProven experience operating high-scale distributed systems in production, including on-call ownership, incident response, and driving systemic reliability improvements.\nStrong 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.\nExcellent written and verbal communication skills.\nNice to have\nExperience designing or tuning local node caching layers to accelerate AI training and inference data access.\nExperience with 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