{"id":2000334,"url":"https://alion.io/job/radianarc-staff-storage-platform-engineer-ai-storage-remote","title":"Staff Storage Platform Engineer (AI Storage) - Remote","company":{"id":3908068,"name":"Radianarc","domain":"radianarc.com","url":"https://alion.io/company/radianarc","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-19T05:45:15Z"}},"role":"DevOps","role_family":"DevOps","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DE","FR","GB","AT","BE","BG","HR","CZ","DK","FI","HU","IE","IT","NL","NO","PL","RO","ES","SE","CH","EE","GR","IS","LV","LI","LT","LU","MT","PT","SK","SI"],"hiring_countries_total":31,"salary":null,"salary_estimate":{"min_usd":113000,"max_usd":262000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":823},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"Bash","optional":false},{"name":"Fine-tuning","optional":false},{"name":"HPC","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-07-22T08:23:51Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-19T07:32:13Z","board_verified":true,"closed_at":null,"days_open":88,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":88},"description":"About Radian Arc.\nWe're specialists in outcome-optimized AI infrastructure - deploying, orchestrating and monetizing GPU compute where data, users and demand actually meet: inside telco networks, at the edge, and in core data centers. Not a generic AI platform. Not a consultancy. We’re the bridge between raw silicon and real-world results.\nWhat impact you will have\nMission: Design, build, and operate the AI storage layer powering large-scale GPU infrastructure, enabling datasets, model artifacts, checkpoints, and inference state to be delivered to compute clusters with extremely high throughput and predictable latency.\nYou will play a key role in architecting and evolving the storage platform across edge and core deployments, supporting the full lifecycle of AI workloads including distributed inference, fine-tuning, and large-scale model training. The role spans multiple storage architectures used across the platform, including hyperconverged storage currently based on StorPool, local NVMe storage for latency-sensitive workloads and edge deployments, and disaggregated AI storage platforms such as VAST Data and Weka.\nAs the first dedicated storage platform role in the organization, this position combines Staff-level architectural ownership, technical direction, and cross-functional influence with hands-on execution across storage design, deployment, performance engineering, troubleshooting, platform integration, and operational improvement.\nA key responsibility of this role is designing and optimizing the storage architecture underlying distributed inference stacks such as NVIDIA Dynamo, llm-d, or similar inference orchestration frameworks. This includes ensuring that storage systems efficiently support inference workloads through optimized dataset access, model artifact distribution, checkpoint handling, and KV-cache persistence. You will design scalable storage systems capable of feeding thousands of GPUs while balancing throughput, latency, resilience, and cost efficiency, and work closely with compute, networking, and platform engineering teams to ensure seamless integration with the platform orchestration layer.\nBecause this is currently the primary storage platform role in the company, the position is intentionally hybrid: you are expected to operate at L6 / Staff in terms of long-term design, standards, cross-team influence, and platform direction, while also directly executing critical storage work that, in a larger organization, would be distributed across multiple engineers.\nThis is a fully remote role, we will consider relevant candidates in all locations.\nWhat you'll need\nCore Experience\nStrong hands-on experience designing and operating distributed storage systems for high-performance compute environments.\nProven experience designing storage architectures for large-scale AI inference or training platforms, including dataset distribution, checkpointing, and KV-cache storage patterns.\nDeep knowledge of the Linux storage and I/O stack.\nStrong understanding of AI workload data access patterns.\nExperience optimizing storage for GPU-accelerated workloads.\n WEKA Data Platform (an enterprise high-performance storage system for AI and HPC)\nFamiliarity with Kubernetes storage integrations such as CSI.\nExperience operating large-scale storage clusters.\nExperience owning both architecture and direct implementation in lean or fast-scaling environments is strongly preferred.\nAdvanced AI Storage Expertise\nThe candidate should have deep expertise in designing and operating storage platforms optimized for GPU-heavy environments and distributed AI workloads.\nThis includes a strong understanding of how training, fine-tuning, and inference systems interact with storage, and how storage architecture affects throughput, latency, concurrency, checkpoint recovery, dataset distribution, and serving performance.\nRelevant expertise includes:\nStrong understanding of storage access patterns for distributed inference and training.\nExperience designing storage platforms that support large dataset ingestion and model artifact distribution at scale.\nPractical experience tuning storage architectures for checkpointing, distributed file access, object access, and high-concurrency inference.\nFamiliarity with storage patterns for KV-cache persistence and retrieval.\nExperience optimizing data locality and reducing unnecessary network movement between storage and compute.\nUnderstanding of how storage performance affects large-scale AI frameworks, model-serving systems, and inference orchestration layers.\nSystems & Troubleshooting\nAbility to debug complex cross-layer issues spanning:\nStorage hardware,\nNetworking,\nLinux kernel and I/O paths,\nFilesystems,\nObject and block storage layers,\nKubernetes integrations,\nDistributed workload behavior.\nStrong knowledge of storage hardware, NVMe devices, storage fabrics, and high-performance data paths.\nExperience designing storage observability systems.\nStrong ability to act as the senior escalation point for ambiguous, high-impact, and multi-domain technical issues.\nAutomation\nStrong automation skills using Python and/or Bash.\nExperience applying software engineering practices to storage automation and operational tooling.\nExperience building reusable tooling, standards, validation patterns, or lifecycle automation that increase leverage across teams.\nLeadership\nProven ability to lead complex technical initiatives across teams.\nComfortable collaborating across engineering, operations, deployment teams, vendors, and platform stakeholders.\nStrong systems-level thinking balancing performance, reliability, scalability, operability, and cost efficiency.\nDemonstrated ability to set architectural direction and drive adoption of engineering standards across an organization.\nProven ability to lead through technical influence across multiple teams and domains, without relying on formal people management authority.\nStrong mentoring capability and ability to raise the technical level of adjacent engineering teams.\nAble to balance short-term execution needs with long-term platform design, operational sustainability, and cost efficiency.\nWhat you’ll do\nStorage Architecture\nDesign scalable AI storage architectures supporting both edge and core deployments.\nDefine storage strategies for distributed inference, fine-tuning, and training workloads.\nArchitect solutions across multiple storage models:\nHyperconverged infrastructure such as StorPool,\nLocal NVMe storage,\nDisaggregated storage systems such as VAST, Weka, and related architectures.\nDefine reference architectures, design principles, and reusable patterns for storage platforms so future deployments follow standards rather than one-off implementations.\nEvaluate trade-offs across throughput, latency, resilience, data locality, cost, and operability, and make clear recommendations to engineering and leadership.\nInfluence the long-term storage roadmap, including architecture choices for edge, core, hyperconverged, and disaggregated environments.\nAI Workload Optimization\nOptimize storage throughput and latency for GPU-heavy clusters.\nDesign data locality strategies to minimize dataset movement across the network.\nBenchmark storage performance under real AI workloads.\nOptimize I/O patterns for large dataset ingestion, checkpointing, and model artifact distribution.\nWork directly with compute teams to ensure storage architecture matches the access patterns of distributed training, fine-tuning, and inference frameworks.\nEstablish performance baselines and validation methods so storage platforms are tested against realistic AI workload behavior rather than only synthetic benchmarks.\nPlatform Integration\nImplement and maintain CSI drivers.\nIntegrate storage platforms with Kubernetes and orchestration systems.\nIntegrate block, object, and shared file storage into the platform.\nDesign multi-tenant storage architectures supporting isolated workloads.\nEnsure storage capabilities are correctly exposed into platform services, workload orchestration, and lifecycle automation.\nDefine standards for how storage should be integrated into Kubernetes-based and platform-managed environments across different deployment models.\nDistributed Storage Systems\nContribute to the design of exabyte-scale storage platforms.\nSupport S3-compatible object storage, distributed file systems, and block storage.\nIntegrate storage clusters into heterogeneous customer environments.\nDesign storage systems with clear fault domains, lifecycle management approaches, scaling paths, and operational boundaries.\nDefine reusable operating patterns for multi-cluster and multi-site storage environments.\nDistributed Inference Storage Architecture\nDesign the storage architecture supporting distributed inference platforms such as NVIDIA Dynamo, llm-d, or similar frameworks.\nOptimize storage performance for large-scale LLM inference workloads.\nDesign efficient strategies for KV-cache persistence and retrieval using distributed storage platforms such as VAST or Weka.\nOptimize storage access patterns for token generation pipelines and high-concurrency inference workloads.\nEnsure inference infrastructure scales efficiently across thousands of GPUs.\nPartner with platform and inference teams to ensure storage design supports evolving inference architectures and avoids becoming a bottleneck in throughput, latency, or concurrency.\nAI Data Path Optimization\nDesign high-performance data paths between GPU clusters and distributed storage.\nOptimize performance using technologies such as:\nGPU Direct Storage,\nRDMA / RoCE,\nNVMe-oF.\nEnsure predictable latency for inference serving workloads.\nDefine architectural approaches for storage-to-GPU data movement that balance performance gains with operational complexity and deployment practicality.\nPerformance Engineering\nWork with technologies such as the following, to maximize data throughput to GPU clusters.:\nRDMA,\nRoCE,\nGPU Direct Storage,\nSPDK,\nNVMe-oF,\nLead storage performance investigations across hardware, network, OS, filesystem, and workload interaction points.\nDrive systematic tuning of storage paths for large-scale GPU environments and define repeatable validation and benchmarking approaches for future deployments.\nReliability & Operations\nImprove the reliability, durability, and observability of the storage stack.\nCollaborate with operations teams to monitor storage systems using telemetry and metrics.\nOptimize performance, latency, and resilience of storage infrastructure.\nLead incident response and root-cause analysis for major storage events and chronic performance issues.\nTranslate operational pain points and incidents into durable design changes, standards, runbooks, and architectural improvements.\nEstablish measurable benchmarks for storage reliability, performance consistency, recovery behavior, and operability across deployments.\nEngineering Execution & Delivery\nLead end-to-end engineering delivery of storage infrastructure from architecture and validation through production rollout.\nSupport practical implementation of storage platforms in both new deployments and existing environments.\nValidate storage BOMs and architecture assumptions together with infrastructure, compute, and deployment teams.\nContribute detailed input into datacenter layouts, node profiles, and storage topology decisions.\nDrive scaling strategies, capacity planning, and storage lifecycle decisions.\nEnsure storage changes are executed safely with minimal customer impact.\nAct as both the architectural owner and the practical execution lead for critical storage initiatives during the build-out phase of the storage function.\nCross-Team Collaboration\nWork closely with compute, networking, platform, DevOps, and operations teams.\nEnsure storage integrates seamlessly into the AI platform architecture.\nAct as the primary storage design authority across the organization, guiding adjacent teams on how storage constraints and capabilities should shape platform decisions...","description_format":"text","description_chars":13444,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Germany","iso":"DE","kind":"region"},{"name":"France","iso":"FR","kind":"region"},{"name":"United Kingdom","iso":"GB","kind":"region"},{"name":"Austria","iso":"AT","kind":"region"},{"name":"Belgium","iso":"BE","kind":"region"},{"name":"Bulgaria","iso":"BG","kind":"region"},{"name":"Croatia","iso":"HR","kind":"region"},{"name":"Czech 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