{"id":2910559,"url":"https://alion.io/job/amd-2027-phd-ai-training-systems-and-performance-engineer-internco-op","title":"2027 PhD AI Training Systems and Performance Engineer Intern/Co-Op","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":75,"open_postings":163,"ghost_share":0,"stale_share":0.994,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Early Careers","role_family":"Early Careers","seniority":"intern","employment_type":"internship","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":56000,"max_usd":91000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":372},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Fine-tuning","optional":false},{"name":"HPC","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"NCCL","optional":false},{"name":"OpenMP","optional":false},{"name":"PyTorch","optional":false},{"name":"ROCm","optional":false},{"name":"Triton","optional":false},{"name":"C++","optional":true},{"name":"JAX","optional":true},{"name":"Python","optional":true},{"name":"SGLang","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-05T16:49:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-24T18:45:49Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"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.\nAs an AMD intern and co-op, you’ll be placed at the epicenter of the AI ecosystem, working alongside experts and industry pioneers. You’ll do important work, learn new skills, expand your network, and gain real-world experience on projects that impact millions of end-users worldwide. Whether you’re an undergrad or a PhD student, your contributions matter-and your experience here will be a launchpad for what comes next.\nJOB DETAILS:\nLocation: San Jose, CA or Santa Clara,CA\n Onsite: This role requires the student to work full time (40 hours a week), onsite work structure throughout the duration of the co-op/intern term\nDuration:\nSpring/Summer Co-op: January 25, 2027 - August 13, 2027\nSummer Internship: \nSemester Students: May 24, 2027 - August 13, 2027 \nQuarter Students: June 21, 2027 - September 10, 2027\nSummer/Fall Co-op: \nSemester Students: May 24, 2027 - December 10, 2027 \nQuarter Students: June 21, 2027 - December 10, 2027\n\nWHAT YOU WILL BE DOING:\nWe are seeking a highly motivated AI Training Systems & Performance Engineering PhD Intern/Co-Op to join our team. In this role, you will help accelerate the adoption and optimization of cutting-edge AI training workloads on AMD Instinct™ GPUs while contributing to the next generation of AI software performance solutions.\nYou will work alongside software engineers, architects, and AI specialists to bring up new training workloads, analyze performance bottlenecks, and develop innovative tooling that improves scalability, efficiency, and developer productivity.\nWe will involve you in developing and optimizing large-scale AI training and fine-tuning workloads running on AMD GPU platforms.\nYou will help bring up newly released foundation models and training frameworks, adapting implementations and training recipes for AMD hardware while establishing reproducible correctness and performance baselines.\nWe will work with you to profile and analyze AI workloads, identifying bottlenecks across GPUs, CPUs, memory systems, networking, and communication infrastructure.\nYou will develop tools and workflows that automate training setup, debugging, performance analysis, and optimization using LLM-powered agents and agentic AI techniques.\nYou will investigate and implement optimization strategies that improve training throughput, GPU utilization, memory efficiency, and scalability across distributed multi-GPU environments.\nWe will expose you to advanced profiling and performance analysis tools to benchmark and tune AI frameworks, libraries, SDKs, and applications running on AMD platforms.\nYou will collaborate with software engineers and architects to evaluate emerging AI models, distributed training techniques, and performance optimization opportunities.\nYour work will help transform successful experiments into reusable workflows, best practices, and software improvements that enhance out-of-the-box AI training performance on AMD hardware.\nWHO WE ARE LOOKING FOR:\nCurrently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, or a related technical discipline.\nStrong programming experience in Python and/or C++.\nHands-on experience implementing, training, and debugging deep learning models using frameworks such as PyTorch, JAX, TensorFlow, vLLM, or SGLang.\nExperience with one or more of the following areas:Distributed training systems\nData, tensor, pipeline, expert, or context parallelism\nGPU performance optimization\nAI systems software\nHigh-performance computing (HPC)\nLarge language model training and fine-tuning\nAgentic AI or LLM-powered automation\n\nUnderstanding of transformer-based architectures, mixture-of-experts models, and modern LLM training techniques.\nExperience profiling workloads using performance analysis tools such as PyTorch Profiler, ROCm Profiler, VTune, Nsight, or similar tools.\nFamiliarity with distributed training technologies and communication libraries such as MPI, NCCL/RCCL, OpenMP, or related frameworks.\nUnderstanding of GPU architecture, memory systems, communication bottlenecks, and performance tuning methodologies.\nExperience identifying and resolving compute, memory, data-loading, or communication bottlenecks in large-scale AI workloads is preferred.\nExperience with ROCm, HIP, Triton, GPU kernel optimization, or AI systems software development is a plus.\nPublications in AI, Machine Learning, High Performance Computing, Computer Architecture, or related research areas are a plus.\nNote: By submitting your application, you are indicating your interest in AMD intern positions. We are recruiting for multiple positions, and if your experience aligns with any of our intern opportunities, a recruiter will contact you.\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.\nAs an AMD intern and co-op, you’ll be placed at the epicenter of the AI ecosystem, working alongside experts and industry pioneers. You’ll do important work, learn new skills, expand your network, and gain real-world experience on projects that impact millions of end-users worldwide. Whether you’re an undergrad or a PhD student, your contributions matter-and your experience here will be a launchpad for what comes next.\nJOB DETAILS:\nLocation: San Jose, CA or Santa Clara,CA\n Onsite: This role requires the student to work full time (40 hours a week), onsite work structure throughout the duration of the co-op/intern term\nDuration:\nSpring/Summer Co-op: January 25, 2027 - August 13, 2027\nSummer Internship: \nSemester Students: May 24, 2027 - August 13, 2027 \nQuarter Students: June 21, 2027 - September 10, 2027\nSummer/Fall Co-op: \nSemester Students: May 24, 2027 - December 10, 2027 \nQuarter Students: June 21, 2027 - December 10, 2027\n\nWHAT YOU WILL BE DOING:\nWe are seeking a highly motivated AI Training Systems & Performance Engineering PhD Intern/Co-Op to join our team. In this role, you will help accelerate the adoption and optimization of cutting-edge AI training workloads on AMD Instinct™ GPUs while contributing to the next generation of AI software performance solutions.\nYou will work alongside software engineers, architects, and AI specialists to bring up new training workloads, analyze performance bottlenecks, and develop innovative tooling that improves scalability, efficiency, and developer productivity.\nWe will involve you in developing and optimizing large-scale AI training and fine-tuning workloads running on AMD GPU platforms.\nYou will help bring up newly released foundation models and training frameworks, adapting implementations and training recipes for AMD hardware while establishing reproducible correctness and performance baselines.\nWe will work with you to profile and analyze AI workloads, identifying bottlenecks across GPUs, CPUs, memory systems, networking, and communication infrastructure.\nYou will develop tools and workflows that automate training setup, debugging, performance analysis, and optimization using LLM-powered agents and agentic AI techniques.\nYou will investigate and implement optimization strategies that improve training throughput, GPU utilization, memory efficiency, and scalability across distributed multi-GPU environments.\nWe will expose you to advanced profiling and performance analysis tools to benchmark and tune AI frameworks, libraries, SDKs, and applications running on AMD platforms.\nYou will collaborate with software engineers and architects to evaluate emerging AI models, distributed training techniques, and performance optimization opportunities.\nYour work will help transform successful experiments into reusable workflows, best practices, and software improvements that enhance out-of-the-box AI training performance on AMD hardware.\nWHO WE ARE LOOKING FOR:\nCurrently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, or a related technical discipline.\nStrong programming experience in Python and/or C++.\nHands-on experience implementing, training, and debugging deep learning models using frameworks such as PyTorch, JAX, TensorFlow, vLLM, or SGLang.\nExperience with one or more of the following areas:Distributed training systems\nData, tensor, pipeline, expert, or context parallelism\nGPU performance optimization\nAI systems software\nHigh-performance computing (HPC)\nLarge language model training and fine-tuning\nAgentic AI or LLM-powered automation\n\nUnderstanding of transformer-based architectures, mixture-of-experts models, and modern LLM training techniques.\nExperience profiling workloads using performance analysis tools such as PyTorch Profiler, ROCm Profiler, VTune, Nsight, or similar tools.\nFamiliarity with distributed training technologies and communication libraries such as MPI, NCCL/RCCL, OpenMP, or related frameworks.\nUnderstanding of GPU architecture, memory systems, communication bottlenecks, and performance tuning methodologies.\nExperience identifying and resolving compute, memory, data-loading, or communication bottlenecks in large-scale AI workloads is preferred.\nExperience with ROCm, HIP, Triton, GPU kernel optimization, or AI systems software development is a plus.\nPublications in AI, Machine Learning, High Performance Computing, Computer Architecture, or related research areas are a plus.\nNote: By submitting your application, you are indicating your interest in AMD intern positions. We are recruiting for multiple positions, and if your experience aligns with any of our intern opportunities, a recruiter will contact you.\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...","description_format":"text","description_chars":12008,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"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":"2026-10-20T09:28:01Z"}],"visa":[],"liveness":{"score":51,"band":"ok","label":"Likely open","p_open":1,"p_active":0.568,"p_room":0.9,"age_days":18,"expected_fill_days":28,"reasons":["conf:3","stale_co","velocity","win:mid","comp:junior,brand"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/amd-2027-phd-ai-training-systems-and-performance-engineer-internco-op","json_url":"https://alion.io/job/amd-2027-phd-ai-training-systems-and-performance-engineer-internco-op.json","meta":{"generated_at":"2026-10-24T22:07:09Z","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":32397,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"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-2027-phd-ai-training-systems-and-performance-engineer-internco-op"},{"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-2027-phd-ai-training-systems-and-performance-engineer-internco-op"},{"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-2027-phd-ai-training-systems-and-performance-engineer-internco-op"},{"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-2027-phd-ai-training-systems-and-performance-engineer-internco-op\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Famd-2027-phd-ai-training-systems-and-performance-engineer-internco-op"}]}