{"id":2911257,"url":"https://alion.io/job/amd-2027-phd-ai-model-optimization-software-engineer-internco-op-2","title":"2027 PhD AI Model Optimization & Software Engineer Intern/Co-op","company":{"id":93,"name":"AMD","domain":"amd.com","url":"https://alion.io/company/amd","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":null},"role":"Backend","role_family":"Backend","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":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"CMake","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Git","optional":false},{"name":"JAX","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"OpenCL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"SGLang","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-09-16T15:09:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-20T22:16:51Z","board_verified":true,"closed_at":null,"days_open":34,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":34},"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 highly motivated AI Model Optimization & Software Engineer Interns/Co-op to join our teams. We are recruiting for multiple opportunities across AI model optimization, framework engineering, performance analysis, GPU computing, distributed systems, and AI software infrastructure.\nDepending on your background, interests, and the needs of the team, you may:\nDevelop, benchmark, and optimize AI software for training, fine-tuning, and inference across CPU, GPU, and accelerator platforms.\nProfile AI workloads, identify hardware and software bottlenecks, and implement performance improvements across compute, memory, communication, framework, and runtime layers.\nDesign, prototype, and optimize GPU or CPU kernels using technologies such as HIP, CUDA, OpenCL, Triton, or related accelerator programming models.\nResearch and implement AI model optimization methods such as quantization, low-precision inference, sparsity, pruning, distillation, and parameter-efficient fine-tuning.\nContribute to AI frameworks, libraries, execution runtimes, and deployment technologies such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or SGLang.\nDevelop or evaluate parallel and distributed computing methods, including collective communication operations and scalable training or inference techniques.\nExplore compiler, graph optimization, kernel-generation, and runtime technologies that improve the efficiency and portability of AI workloads.\nBuild automated evaluation systems, containerized development environments, CI/CD pipelines, internal tools, and developer productivity solutions for AI workflows.\nUse AI-assisted development tools and coding agents to support software development, testing, debugging, and optimization.\nCollaborate with software engineers, researchers, architects, platform teams, and project stakeholders to advance next-generation AI software capabilities.\nWHO WE ARE LOOKING FOR:\nWe recognize that candidates may bring strengths in different areas of AI Model Optimization & AI Software. You are not expected to have experience in every technology or technical area listed below. If you have experience, academic research, project work, or a strong technical interest in one or more of these areas, we encourage you to apply.\nCurrently enrolled in a U.S.-based PhD program in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related technical field.\nProgramming experience in Python and/or C/C++.\nExperience with one or more AI frameworks or runtimes, such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or SGLang.\nExperience, coursework, research, or project work in one or more of the following areas:AI model inference, training, fine-tuning, or performance optimization\nQuantization, low-precision computing, sparsity, pruning, or distillation\nGPU or accelerator kernel development\nAI frameworks, libraries, compilers, graph optimization, or execution runtimes\nPerformance profiling, benchmarking, debugging, or workload analysis\nParallel computing, distributed systems, or collective communications\nMLOps, DevOps, automated evaluation, CI/CD, or containerized environments\nAI-assisted coding, coding agents, or agentic development workflows\n\nFamiliarity with software development tools such as Git, CMake, Make, Conda, Docker, or related development and build technologies.\nResearch publications, preprints, open-source contributions, or participation in the AI and machine learning developer community are preferred but not required.\nIf you are excited about advancing AI software and bring experience in at least one of the areas above, we encourage you to apply.\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 highly motivated AI Model Optimization & Software Engineer Interns/Co-op to join our teams. We are recruiting for multiple opportunities across AI model optimization, framework engineering, performance analysis, GPU computing, distributed systems, and AI software infrastructure.\nDepending on your background, interests, and the needs of the team, you may:\nDevelop, benchmark, and optimize AI software for training, fine-tuning, and inference across CPU, GPU, and accelerator platforms.\nProfile AI workloads, identify hardware and software bottlenecks, and implement performance improvements across compute, memory, communication, framework, and runtime layers.\nDesign, prototype, and optimize GPU or CPU kernels using technologies such as HIP, CUDA, OpenCL, Triton, or related accelerator programming models.\nResearch and implement AI model optimization methods such as quantization, low-precision inference, sparsity, pruning, distillation, and parameter-efficient fine-tuning.\nContribute to AI frameworks, libraries, execution runtimes, and deployment technologies such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or SGLang.\nDevelop or evaluate parallel and distributed computing methods, including collective communication operations and scalable training or inference techniques.\nExplore compiler, graph optimization, kernel-generation, and runtime technologies that improve the efficiency and portability of AI workloads.\nBuild automated evaluation systems, containerized development environments, CI/CD pipelines, internal tools, and developer productivity solutions for AI workflows.\nUse AI-assisted development tools and coding agents to support software development, testing, debugging, and optimization.\nCollaborate with software engineers, researchers, architects, platform teams, and project stakeholders to advance next-generation AI software capabilities.\nWHO WE ARE LOOKING FOR:\nWe recognize that candidates may bring strengths in different areas of AI Model Optimization & AI Software. You are not expected to have experience in every technology or technical area listed below. If you have experience, academic research, project work, or a strong technical interest in one or more of these areas, we encourage you to apply.\nCurrently enrolled in a U.S.-based PhD program in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related technical field.\nProgramming experience in Python and/or C/C++.\nExperience with one or more AI frameworks or runtimes, such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or SGLang.\nExperience, coursework, research, or project work in one or more of the following areas:AI model inference, training, fine-tuning, or performance optimization\nQuantization, low-precision computing, sparsity, pruning, or distillation\nGPU or accelerator kernel development\nAI frameworks, libraries, compilers, graph optimization, or execution runtimes\nPerformance profiling, benchmarking, debugging, or workload analysis\nParallel computing, distributed systems, or collective communications\nMLOps, DevOps, automated evaluation, CI/CD, or containerized environments\nAI-assisted coding, coding agents, or agentic development workflows\n\nFamiliarity with software development tools such as Git, CMake, Make, Conda, Docker, or related development and build technologies.\nResearch publications, preprints, open-source contributions, or participation in the AI and machine learning developer community are preferred but not required.\nIf you are excited about advancing AI software and bring experience in at least one of the areas above, we encourage you to apply.\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. 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