{"id":3387826,"url":"https://alion.io/job/amd-ai-performance-modeling-engineer","title":"AI Performance Modeling Engineer","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":78,"open_postings":167,"ghost_share":0,"stale_share":0.898,"repost_share":0,"time_to_fill_p50_days":34,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-10-28T06:06:00Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-31T02:11:15Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"WHAT YOU DO AT AMD CHANGES EVERYTHING\nAt AMD, our mission is to build great products that accelerate next-generation computing experiences-from AI and data centers, to PCs, gamingand embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuityand a shared passion to create something extraordinary. When you join AMD, you’lldiscover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges-striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.Together, we advance your career.\nThe Role\nWe're building GPUs to run the most demanding AI in the world - Transformers, LLMs, and large vision models - and we need an experienced hands-on engineer who can tell us how fast those workloads will run before the silicon exists. This is an AI performance projection/modeling role: your job is to model AI workloads on our GPU architecture, predict their performance with confidence, and use those projections to shape the hardware we build.\nYou'll work across the full modeling spectrum - analytical models for fast architectural what-ifs, and architectural and cycle-accurate simulators for precision - choosing the right level of fidelity for the question at hand. Because these workloads live on the GPU, you'll need real GPU architecture depth to do it.\nIf you think in rooflines, arithmetic intensity, tensor/pipeline/data parallelism, and KV-cache bandwidth budgets - and you want your models to change the microarchitecture, not just describe it - this is the role.\nWhat You'll Do\nProject AI workload performance (your core mission)\nBuild and use models - analytical, architectural, and cycle-accurate - to project the performance of generative AI and LLM workloads (attention, GEMMs, collectives, training and inference, vision models etc) on our GPU architecture.\nChoose the right fidelity for the question: analytical bounds and rooflines for rapid trade-off exploration, architectural and cycle-accurate simulation for precise answers.\nFind where performance will be won or lost - compute-engine occupancy, L1/L2 hierarchy, memory bandwidth, SoC fabric - and quantify the impact of each.\nCharacterize workloads & shape the hardware\nCharacterize AI workloads end to end - trace and profile execution, extract structural footprints, and translate them into microarchitectural bottlenecks and performance bounding boxes.\nRun architecture experiments to evaluate GPU configurations, scaling limits, and trade-offs against real AI workloads, backing every recommendation with sensitivity analysis and data.\nExtend and maintain the cycle-accurate / cycle-approximate simulators for the GPU subsystems AI workloads depend on - compute engines, cache hierarchies, memory subsystems, interconnects.\nProve it on silicon (post-silicon)\nRun AI workloads on early silicon, capture telemetry, and correlate results back to your pre-silicon models to sharpen fidelity.\nRoot-cause AI performance mismatches on physical silicon using low-level performance counters, register dumps, and Linux tracing.\nCollaborate across the stack\nPartner with compiler, runtime, and framework teams to turn model insights into optimizations, and to ground your projections in how workloads actually execute.\nAct as the bridge between architecture/RTL and software, translating AI workload requirements into concrete architectural constraints.\nWhat You'll Bring\n AI workload expertise: Deep, hands-on understanding of LLM and Transformer performance - scaling techniques, parallel training/inference strategies (tensor, pipeline, data parallelism), kernel execution graphs - and how they map to hardware.\nPerformance modeling breadth: Hands-on experience with analytical performance models and execution-driven / architectural / cycle-accurate simulators, and sound judgment about which to reach for. Track record of turning models into decisions.\n GPU architecture depth: Strong command of GPU execution pipelines, SIMD/SIMT models, cache hierarchy, memory technologies, and high-bandwidth interconnects (PCIe, custom fabrics).\nEngineering craft: Expert modern C++ and production-grade Python.\nExecution: Excellent structured problem-solving; able to turn noisy simulation data into crisp, actionable findings for engineering stakeholders.\nNice to have: Experience with NPU / AI-accelerator architecture, or modeling AI workloads on dedicated inference/training accelerators.\n#LI-AA1Benefits 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're building GPUs to run the most demanding AI in the world - Transformers, LLMs, and large vision models - and we need an experienced hands-on engineer who can tell us how fast those workloads will run before the silicon exists. This is an AI performance projection/modeling role: your job is to model AI workloads on our GPU architecture, predict their performance with confidence, and use those projections to shape the hardware we build.\nYou'll work across the full modeling spectrum - analytical models for fast architectural what-ifs, and architectural and cycle-accurate simulators for precision - choosing the right level of fidelity for the question at hand. Because these workloads live on the GPU, you'll need real GPU architecture depth to do it.\nIf you think in rooflines, arithmetic intensity, tensor/pipeline/data parallelism, and KV-cache bandwidth budgets - and you want your models to change the microarchitecture, not just describe it - this is the role.\nWhat You'll Do\nProject AI workload performance (your core mission)\nBuild and use models - analytical, architectural, and cycle-accurate - to project the performance of generative AI and LLM workloads (attention, GEMMs, collectives, training and inference, vision models etc) on our GPU architecture.\nChoose the right fidelity for the question: analytical bounds and rooflines for rapid trade-off exploration, architectural and cycle-accurate simulation for precise answers.\nFind where performance will be won or lost - compute-engine occupancy, L1/L2 hierarchy, memory bandwidth, SoC fabric - and quantify the impact of each.\nCharacterize workloads & shape the hardware\nCharacterize AI workloads end to end - trace and profile execution, extract structural footprints, and translate them into microarchitectural bottlenecks and performance bounding boxes.\nRun architecture experiments to evaluate GPU configurations, scaling limits, and trade-offs against real AI workloads, backing every recommendation with sensitivity analysis and data.\nExtend and maintain the cycle-accurate / cycle-approximate simulators for the GPU subsystems AI workloads depend on - compute engines, cache hierarchies, memory subsystems, interconnects.\nProve it on silicon (post-silicon)\nRun AI workloads on early silicon, capture telemetry, and correlate results back to your pre-silicon models to sharpen fidelity.\nRoot-cause AI performance mismatches on physical silicon using low-level performance counters, register dumps, and Linux tracing.\nCollaborate across the stack\nPartner with compiler, runtime, and framework teams to turn model insights into optimizations, and to ground your projections in how workloads actually execute.\nAct as the bridge between architecture/RTL and software, translating AI workload requirements into concrete architectural constraints.\nWhat You'll Bring\n AI workload expertise: Deep, hands-on understanding of LLM and Transformer performance - scaling techniques, parallel training/inference strategies (tensor, pipeline, data parallelism), kernel execution graphs - and how they map to hardware.\nPerformance modeling breadth: Hands-on experience with analytical performance models and execution-driven / architectural / cycle-accurate simulators, and sound judgment about which to reach for. Track record of turning models into decisions.\n GPU architecture depth: Strong command of GPU execution pipelines, SIMD/SIMT models, cache hierarchy, memory technologies, and high-bandwidth interconnects (PCIe, custom fabrics).\nEngineering craft: Expert modern C++ and production-grade Python.\nExecution: Excellent structured problem-solving; able to turn noisy simulation data into crisp, actionable findings for engineering stakeholders.\nNice to have: Experience with NPU / AI-accelerator architecture, or modeling AI workloads on dedicated inference/training accelerators.\n#LI-AA1Benefits 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.","description_format":"text","description_chars":10385,"description_truncated":false,"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":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-28T08:32:14Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":1,"expected_fill_days":34,"reasons":["conf:1","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-30T05:48:23Z"},"pay":null,"html_url":"https://alion.io/job/amd-ai-performance-modeling-engineer","json_url":"https://alion.io/job/amd-ai-performance-modeling-engineer.json","meta":{"generated_at":"2026-10-31T02:51:16Z","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":4019,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-01T00:00:00Z"}},"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-ai-performance-modeling-engineer"},{"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-ai-performance-modeling-engineer"},{"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-ai-performance-modeling-engineer"},{"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-ai-performance-modeling-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Famd-ai-performance-modeling-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3387826}},{"tool":"search_jobs","arguments":{"company":"amd.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3387826},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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