{"id":606790,"url":"https://alion.io/job/ddn-senior-staff-engineer-ai-data-path","title":"Senior Staff Engineer - AI Data Path","company":{"id":672909,"name":"DDN","domain":"ddn.com","url":"https://alion.io/company/ddn","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":97,"open_postings":5,"ghost_share":0,"stale_share":0,"repost_share":0.2,"time_to_fill_p50_days":42,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"InfiniBand","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"HPC","optional":true},{"name":"LLM","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-07-31T20:05:47Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-17T23:29:52Z","board_verified":true,"closed_at":null,"days_open":78,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":78},"description":"DDN is seeking a highly experienced Senior Staff Engineer specializing in AI Data Path & Storage to lead hands-on development and integration of advanced storage systems with next-generation AI inference pipelines. This role involves coding, prototyping, and rapidly iterating on solutions in close collaboration with architects to design and deliver high-performance data movement architectures. You will leverage NVIDIA’s NIXL (Inference Transfer Library) alongside the Infinia Data Intelligence Platform to enable ultra-low-latency, high-throughput data movement across GPU, memory, and distributed storage layers, including workloads involving KV cache management and vector database retrieval. The ideal candidate brings deep expertise in distributed storage, GPU data paths, and large-scale system optimization, with a proven track record of building and shipping production-grade AI infrastructure.\nKey Responsibilities\nLead the design and implementation of high-performance data movement pipelines using NVIDIA NIXL across GPU, CPU, and storage tiers.\n\nArchitect and drive integration of DDN Infinia with GPU-accelerated inference platforms for large-scale, real-time AI workloads.\n\nOwn end-to-end optimization of I/O paths between GPU memory and storage using technologies such as NVIDIA GPUDirect Storage, RDMA, and NVMe-over-Fabrics.\n\nDefine and implement multi-tier storage architectures (NVMe, SSD, object storage) optimized for inference latency, throughput, and scalability.\n\nLead development of advanced KV cache management strategies, including offloading, prefetching, and persistence across distributed storage layers.\n\nPartner with AI/ML engineering teams to optimize inference performance in frameworks such as PyTorch and TensorFlow.\n\nEstablish benchmarking frameworks and lead performance tuning efforts for storage and data movement in production inference environments.\n\nDiagnose and resolve complex system bottlenecks across storage, networking, and GPU subsystems.\n\nInfluence architecture decisions for distributed inference systems, ensuring scalability, resilience, and efficient data locality.\n\nDrive engineering excellence through best practices in observability, performance monitoring, automation, and reliability engineering.\n\nMentor junior engineers and provide technical leadership across cross-functional teams.\n\nRequired Qualifications\nBachelor’s or Master’s degree in Computer Science, Engineering, or a related field.\n\n12+ years of experience in storage systems, distributed systems, or performance engineering.\n\nProven track record of architecting and delivering large-scale, high-performance infrastructure systems.\n\nDeep expertise in distributed storage architectures (object storage, scalable file systems, or cloud-native storage platforms).\n\nStrong understanding of Linux I/O stack, filesystem internals, and storage protocols.\n\nExtensive hands-on experience with NVMe, SSD optimization, and high-performance storage environments.\n\nStrong experience with RDMA, InfiniBand, or other high-speed data transfer technologies.\n\nSolid understanding of GPU computing concepts and CPU-GPU data movement patterns.\n\nProficiency in Python and/or C/C++, with advanced debugging, profiling, and performance tuning skills.\n\nDemonstrated ability to optimize latency-sensitive, high-throughput production systems.\n\nPreferred Skills\nHands-on experience with NVIDIA NIXL or similar data movement frameworks.\n\nExperience with GPU-aware storage pipelines and GPUDirect Storage.\n\nStrong understanding of AI inference systems, LLM serving architectures, and KV cache optimization.\n\nExperience with Retrieval-Augmented Generation (RAG) pipelines and open vector search ecosystems.\n\nBackground in high-performance computing (HPC) or hyperscale distributed environments.\n\nExpertise in caching strategies, memory tiering, and data locality optimization.\n\nExperience designing disaggregated compute and storage architectures.\n\nWhat You’ll Work On\nLeading the evolution of storage systems into GPU-native data layers for AI inference\n\nBuilding next-generation distributed AI infrastructure using NIXL and Infinia\n\nDriving performance breakthroughs in real-time LLM inference at scale\n\nDesigning storage architectures for large-scale AI datasets and retrieval systems","description_format":"text","description_chars":4283,"description_truncated":false,"requirements":{"experience_years_min":12,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United 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