{"id":2910019,"url":"https://alion.io/job/amd-lead-hpc-ai-infrastructure-architect","title":"Lead HPC & AI Infrastructure Architect","company":{"id":93,"name":"AMD","domain":"amd.com","url":"https://alion.io/company/amd","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":{"grade":"B","score":80,"open_postings":157,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-22T06:10:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":180000,"max_usd":369000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":730},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"HPC","optional":false},{"name":"Kubernetes","optional":false},{"name":"Least Privilege","optional":false},{"name":"SLURM","optional":false},{"name":"Ansible","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Bash","optional":true},{"name":"C","optional":true},{"name":"Configuration Management","optional":true},{"name":"Git","optional":true},{"name":"Grafana","optional":true},{"name":"InfiniBand","optional":true},{"name":"Linux","optional":true},{"name":"LLM","optional":true},{"name":"MPI","optional":true},{"name":"Prometheus","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"ROCm","optional":true},{"name":"SGLang","optional":true},{"name":"Terraform","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-13T13:10:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-23T03:56:28Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"ADVANCE YOUR CAREER. ADVANCE THE WORLD.\nAt AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.\nWhether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger - technology that moves the world forward. Join us and, together, we’ll advance your career.\nTHE ROLE\nWe are seeking a Lead HPC & AI Infrastructure Architect to lead the architecture and technical direction of large-scale CPU and GPU platforms across on-premises infrastructure, public cloud, and specialized GPU cloud providers. You will translate scientific computing, engineering, and AI requirements into secure, reliable, scalable, and cost-effective platforms for tightly coupled HPC workloads, distributed model training, and inference services.\nThis hands-on leadership role spans design, workload validation, deployment, and lifecycle management. You will provide technical direction to infrastructure engineering and SRE teams and collaborate with researchers, AI engineers, security teams, cloud providers, hardware vendors, and data center teams to deliver production-ready platforms.\nTHE PERSON\nYou combine strong systems expertise with practical architectural judgment. You approach complex technical problems collaboratively, validate decisions with measurable results, and communicate tradeoffs clearly. You work effectively across engineering, operations, security, and research teams; mentor engineers with varied backgrounds and experience levels; and balance performance, reliability, security, delivery timelines, and cost. You can lead initiatives with appropriate autonomy while engaging the partners needed to move from requirements through production acceptance.\nKEY RESPONSIBILITIES\nLead the HPC and AI infrastructure roadmap, reference architectures, and standards for CPU/GPU clusters. Define workload placement across bare metal, virtual machines, Slurm, and Kubernetes.\nLead designs covering CPU/NUMA and GPU topology, accelerator interconnects, networking, storage, and identity. Establish validated hardware, firmware, driver, and runtime baselines.\nArchitect RDMA-capable fabrics and storage tiers. Validate collective communication, parallel I/O, dataset delivery, and checkpoint performance at target scale.\nDesign resource sharing through Slurm accounting, fair-share, quotas, QoS, preemption, and tenant isolation. Enable reproducible environments, self-service onboarding, and inference capacity planning.\nStandardize provisioning, configuration, and fleet upgrades through infrastructure as code, automation, and CI/CD; manage images, containers, and configuration drift.\nDefine production acceptance criteria, service-level objectives, and fleet observability. Partner with SRE teams on fault isolation, job recovery, checkpoint/restart, incident analysis, and capacity planning.\nBenchmark HPC and distributed AI workloads; improve scaling efficiency, throughput, inference latency, GPU utilization, reliability, and cost efficiency through bottleneck analysis.\nIncorporate federated identity, least-privilege access, tenant and network isolation, secrets management, controlled container privileges, audit logging, and vulnerability management into platform designs.\nLead architecture reviews, proofs of concept, provider evaluations, and complex technical problem solving. Document decisions and tradeoffs, mentor engineers, and align delivery with stakeholders and infrastructure partners.\nPREFERRED EXPERIENCE\nSignificant experience in infrastructure, cloud engineering, or distributed systems, including demonstrated experience designing and delivering production HPC or GPU platforms. Equivalent practical experience is welcome.\nExperience leading large-scale cluster deployments involving hundreds of nodes, substantial multi-GPU environments, or systems of comparable technical complexity, with measurable improvements in workload performance, reliability, utilization, or cost.\nExperience with a major cloud platform such as Azure, OCI, or AWS, including integration between cloud and on-premises systems. Familiarity with specialized GPU providers such as Crusoe or TensorWave is beneficial.\nStrong Linux troubleshooting skills across CPU/NUMA topology, PCIe, GPU connectivity, drivers, virtualization, containers, and distributed runtime environments.\nHands-on experience with Slurm scheduling and resource governance, plus Kubernetes or comparable orchestration experience. Understanding of MPI, distributed training, collective communication, and AI inference infrastructure.\nExperience with GPU computing platforms and distributed AI workloads. Experience with AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads, including compatibility validation and performance troubleshooting, is particularly relevant.\nHPC networking and storage experience, including RDMA, RoCE or InfiniBand, topology and oversubscription tradeoffs, parallel I/O, data placement, checkpointing, and enterprise connectivity.\nExperience with parallel and shared storage technologies such as Lustre, IBM Storage Scale, BeeGFS, NFS, VAST, or comparable technologies, as well as object storage and cluster acceptance benchmarking.\nExperience with Terraform or equivalent infrastructure as code, Ansible or comparable configuration management, Python or Bash, and Git-based CI/CD. Familiarity with Azure CycleCloud, AWS ParallelCluster, or comparable cluster provisioning frameworks is beneficial.\nExperience implementing observability and SRE practices using Prometheus, Grafana, or comparable technologies, including secure access, resource controls, fault recovery, and production readiness.\nAbility to lead cross-functional technical work, clearly communicate architecture decisions and tradeoffs, and guide projects from requirements through production acceptance.\nExperience coordinating with hardware and data center teams on rack power, liquid cooling, facility capacity, and other physical constraints affecting GPU platform design.\nPreferred qualifications\nHands-on experience with AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads, including compatibility validation, benchmarking, and performance troubleshooting.\nExperience with Azure, OCI, Crusoe, TensorWave, or comparable GPU environments and cluster provisioning frameworks such as Azure CycleCloud or AWS ParallelCluster.\nExperience with Lustre, IBM Storage Scale, BeeGFS, VAST, or comparable high-performance storage platforms, plus network, storage, and collective communication benchmarks used for cluster acceptance.\nExperience with LLM serving platforms such as vLLM or SGLang, model parallelism, inference capacity planning, and usage accounting or cost allocation.\nUnderstanding of rack power, liquid cooling, and facility capacity constraints, with experience coordinating platform requirements with hardware and data center teams.\nACADEMIC CREDENTIALS\nBachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.\nThis role is not eligible for visa sponsorship.\nBenefits offered are described: AMD benefits at a glance.\nAMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.\nAMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.\n\nThis posting is for an existing vacancy.\nTHE ROLE\nWe are seeking a Lead HPC & AI Infrastructure Architect to lead the architecture and technical direction of large-scale CPU and GPU platforms across on-premises infrastructure, public cloud, and specialized GPU cloud providers. You will translate scientific computing, engineering, and AI requirements into secure, reliable, scalable, and cost-effective platforms for tightly coupled HPC workloads, distributed model training, and inference services.\nThis hands-on leadership role spans design, workload validation, deployment, and lifecycle management. You will provide technical direction to infrastructure engineering and SRE teams and collaborate with researchers, AI engineers, security teams, cloud providers, hardware vendors, and data center teams to deliver production-ready platforms.\nTHE PERSON\nYou combine strong systems expertise with practical architectural judgment. You approach complex technical problems collaboratively, validate decisions with measurable results, and communicate tradeoffs clearly. You work effectively across engineering, operations, security, and research teams; mentor engineers with varied backgrounds and experience levels; and balance performance, reliability, security, delivery timelines, and cost. You can lead initiatives with appropriate autonomy while engaging the partners needed to move from requirements through production acceptance.\nKEY RESPONSIBILITIES\nLead the HPC and AI infrastructure roadmap, reference architectures, and standards for CPU/GPU clusters. Define workload placement across bare metal, virtual machines, Slurm, and Kubernetes.\nLead designs covering CPU/NUMA and GPU topology, accelerator interconnects, networking, storage, and identity. Establish validated hardware, firmware, driver, and runtime baselines.\nArchitect RDMA-capable fabrics and storage tiers. Validate collective communication, parallel I/O, dataset delivery, and checkpoint performance at target scale.\nDesign resource sharing through Slurm accounting, fair-share, quotas, QoS, preemption, and tenant isolation. Enable reproducible environments, self-service onboarding, and inference capacity planning.\nStandardize provisioning, configuration, and fleet upgrades through infrastructure as code, automation, and CI/CD; manage images, containers, and configuration drift.\nDefine production acceptance criteria, service-level objectives, and fleet observability. Partner with SRE teams on fault isolation, job recovery, checkpoint/restart, incident analysis, and capacity planning.\nBenchmark HPC and distributed AI workloads; improve scaling efficiency, throughput, inference latency, GPU utilization, reliability, and cost efficiency through bottleneck analysis.\nIncorporate federated identity, least-privilege access, tenant and network isolation, secrets management, controlled container privileges, audit logging, and vulnerability management into platform designs.\nLead architecture reviews, proofs of concept, provider evaluations, and complex technical problem solving. Document decisions and tradeoffs, mentor engineers, and align delivery with stakeholders and infrastructure partners.\nPREFERRED EXPERIENCE\nSignificant experience in infrastructure, cloud engineering, or distributed systems, including demonstrated experience designing and delivering production HPC or GPU platforms. Equivalent practical experience is welcome.\nExperience leading large-scale cluster deployments involving hundreds of nodes, substantial multi-GPU environments, or systems of comparable technical complexity, with measurable improvements in workload performance, reliability, utilization, or cost.\nExperience with a major cloud platform such as Azure, OCI, or AWS, including integration between cloud...","description_format":"text","description_chars":15978,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-20T09:19:33Z"}],"visa":[],"liveness":{"score":53,"band":"ok","label":"Likely open","p_open":1,"p_active":0.592,"p_room":0.9,"age_days":8,"expected_fill_days":15,"reasons":["conf:1","stale_co","velocity","win:mid","comp:brand"],"computed_at":"2026-10-22T06:10:10Z"},"pay":null,"html_url":"https://alion.io/job/amd-lead-hpc-ai-infrastructure-architect","json_url":"https://alion.io/job/amd-lead-hpc-ai-infrastructure-architect.json","meta":{"generated_at":"2026-10-23T04:52:18Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":7938,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-24T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":93},"rest":"https://alion.io/mcp/rest/get_company?id=93"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Famd-lead-hpc-ai-infrastructure-architect"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Famd-lead-hpc-ai-infrastructure-architect"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Famd-lead-hpc-ai-infrastructure-architect"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/amd-lead-hpc-ai-infrastructure-architect\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Famd-lead-hpc-ai-infrastructure-architect"}]}