{"id":3401082,"url":"https://alion.io/job/nvidia-senior-performance-research-and-analysis-engineer","title":"Senior Performance Research and Analysis Engineer","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":81,"open_postings":820,"ghost_share":0.024,"stale_share":0.685,"repost_share":0.046,"time_to_fill_p50_days":36,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Yokneam, Israel","Ra'anana, Israel","Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":101000,"max_usd":227000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":8444},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Bash","optional":false},{"name":"HPC","optional":false},{"name":"Linux","optional":false},{"name":"Python","optional":false},{"name":"TCP/IP","optional":false}],"status":"live","first_seen_at":"2026-10-28T14:30:42Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-29T18:26:55Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"NVIDIA’s Performance Group is seeking a technical NIC & DPU Performance Research & Analysis Engineer to drive end-to-end performance strategy, characterization, optimization and HW offload accelerations for next-generation network interfaces and Data Processing Units (DPUs).\nIn this role, you will analyze and accelerate critical networking and storage technologies and protocols powering multi nodes AI clusters. You will profile performance across the complete hardware-to-software execution stack-stretching from DPU Arm CPU subsystems and PCIe to high-speed RDMA, SPDK, and DOCA acceleration pipelines.\nWhat You’ll Be Doing:\nData-Path Optimization: Drive end-to-end benchmark plans, performance characterization, expectations and optimizations for NICs and DPUs deployed in distributed AI and cloud data centers.\nFull-Stack Profiling: Profile low-level memory, PCIe, host CPU, and DPU ASIC execution paths to identify micro-architectural stragglers, queue bottlenecks, and tail-latency outliers.\nStorage & Network Acceleration: Hands-on acceleration and offloading of storage (SPDK, NVMe-oF, VirtIO) and networking protocols (RDMA/RoCEv2, TCP/IP, DOCA).\nAI-Assisted Workflow Automation: Leverage AI agents, autonomous scripts, and automated regression frameworks to scale continuous performance evaluation.\nCross-Functional RCA : Partner closely with Architecture, ASIC, Firmware, and Software engineering groups to conduct rigorous Root Cause Analysis (RCA) and establish performance targets for upcoming pre- and post-silicon platforms.\nWhat We Need to See:\nB.Sc. or M.Sc. in Computer Science, Computer/Electrical Engineering, Software Engineering, or equivalent practical experience.\n5+ years of experience in low-level networking, storage acceleration, and systems-level performance tuning in cloud or HPC environments.\nDirect hands-on experience with at least two of the following: DOCA, SPDK, VirtIO, RDMA/RoCEv2, TCP/IP.\nStrong programming proficiency in C, Python, and Linux shell scripting (Bash).\nMicro-architectural understanding of modern CPU/ASIC memory hierarchies, PCIe Gen 5/6 interconnects, and Linux kernel networking stacks.\nProven track record using AI coding agents or automated script generators to streamline daily engineering workflows.\nWays to Stand Out from the crowd:\nDeep hands-on experience tuning NVIDIA BlueField DPUs, ConnectX SmartNICs, or custom hardware offload engines.\nMastery of the full SPDK & NVMe-oF (NVMe over Fabrics) storage stack over high-speed RDMA transport layers.\nComprehensive system-level understanding across x86 (Intel/AMD) and Arm architectures, PCIe topology, and GPUDirect Storage (GDS).\nProven experience isolating stragglers and tail latency in multi-tenant, cloud-scale environments.","description_format":"text","description_chars":2748,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Servers & Data Center Hardware","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-28T14:30:42Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely 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