{"schemaVersion":"jobsearcher.job.v1","id":"67f5da5b1bff7fb64c7bda9e","url":"https://jobsearcher.com/jobs/67f5da5b1bff7fb64c7bda9e","canonicalUrl":"https://jobsearcher.com/jobs/67f5da5b1bff7fb64c7bda9e","title":"AI Model Optimization Engineer","description":"Overview:\nWHAT 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, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover 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.\nResponsibilities:\nTHE ROLE:\nThe AMD AI Group is looking for a Senior Software Development Engineer to own the end-to-end model execution stack on AMD Instinct GPUs - spanning training infrastructure at scale and high-performance inference serving. This role demands someone who has shipped LLMs on real hardware, written GPU kernels that moved production metrics, and built the systems infrastructure (orchestration, storage, monitoring) that keeps thousands of GPUs productive. You will be instrumental in ensuring AMD GPUs are first-class citizens for frontier model training and inference across current and next-generation Instinct accelerators.\n\nKEY RESPONSIBILITIES:\nTraining Infrastructure & Enablement\nEnable and optimize large-scale model training (LLMs, VLMs, MoE architectures) on AMD Instinct GPU clusters, ensuring correctness, reproducibility, and competitive throughput.\nBuild and maintain training infrastructure: job orchestration, distributed checkpointing, data loading pipelines, and storage optimization for multi-thousand GPU clusters on Kubernetes.\nDebug and resolve training-specific issues including gradient norm explosions, non-deterministic behavior across GPU generations, and compute-communication overlap in distributed training (FSDP, DeepSpeed, Megatron-LM).\nOptimize RCCL collective communication patterns for training workloads, including all-reduce, all-gather, and reduce-scatter across multi-node topologies.\nDevelop monitoring, alerting, and compliance infrastructure to ensure training cluster health, data security, and SLA adherence at scale.\nInference Optimization & Serving\nWrite and optimize high-performance GPU kernels (GEMM, attention, quantized matmul, GPTQ/AWQ) in HIP, Triton, and MLIR targeting AMD Instinct architectures, with demonstrated ability to outperform open-source baselines.\nDrive end-to-end inference enablement on new AMD GPU silicon - be among the first to get frontier models running on each new Instinct generation, creating reproducible guides and reference implementations.\nOptimize inference serving frameworks (vLLM, SGLang, TorchServe) for AMD GPUs: batching strategies, KV-cache management, speculative decoding, and continuous batching for production throughput/latency targets.\nDevelop novel approaches to inference acceleration, including bio-inspired algorithms, SLM-assisted batching, and custom scheduling strategies that exploit AMD hardware characteristics.\nBuild quantization pipelines (FP8, FP6, FP4, GPTQ, AWQ) for production model deployment, ensuring quality-performance tradeoffs are well-characterized across AMD GPU generations.\nCross-Cutting\nDesign observability and debugging tooling: log analysis pipelines, anomaly detection systems, and failure correlation tools for large-scale GPU clusters processing hundreds of terabytes of telemetry per month.\nCollaborate with AMD silicon architecture teams to provide software feedback on next-generation Instinct GPU designs for both training and inference workloads.\nContribute to the open ROCm ecosystem and AMD’s developer experience - SDKs, CI dashboards, documentation, and developer cloud enablement.\nCollaborate closely with multiple teams to deliver key planning solutions and the technology to support them\nHelp contribute to the design and implementation of future architecture for a highly scalable, durable, and innovative system\nWork very closely with dev teams and Project Managers to drive results\n\nPREFERRED EXPERIENCE:\nStrong Industry experience shipping production AI/ML infrastructure, with hands-on work spanning both training and inference.\nProven experience running LLMs on AMD GPUs (ROCm, HIP) or equivalent depth with CUDA, with strong willingness to work on AMD platforms.\nTrack record of writing custom GPU kernels (CUDA, HIP, or Triton) that delivered measurable throughput improvements in production systems.\nStrong systems engineering skills: Kubernetes, container orchestration, distributed storage, and GPU cluster management at scale (1,000+ GPUs).\nProficiency in Python and at least one systems language (C++, Rust, Go, C#) with production-quality software engineering practices.\nDeep understanding of LLM architecture internals: attention mechanisms, KV-cache, quantization schemes, and distributed parallelism strategies (tensor, pipeline, expert parallelism).\nDirect experience enabling frontier models (GPT-4 class) on AMD Instinct hardware end-to-end.\nExpert knowledge and hands-on experience in C, C++\nSolid understanding of object-oriented-design principles\nSolid understanding of Software Engineering principles, Data structure, algorithms, Operating Systems concepts and multithread programming\nExcellent design and code development skills, familiarity with Linux and modern software tools and techniques for development\nGood analytical and problem-solving skills\n\nACADEMIC CREDENTIALS:\nBachelor’s or Master’s degree in Computer/Software Engineering, Computer Science, or related technical discipline\n\nThis role is not eligible for visa sponsorship.\n\n#LI-CJ3\n#LI-Hybrid\n\nQualifications:\nBenefits offered are described: AMD benefits at a glance.\n\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.\n\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.","company":"Advanced Micro Devices","rawCompany":"advanced micro devices","city":"Santa Clara","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-04-14T11:17:13.760Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AI Model Optimization Engineer","description":"Overview:\nWHAT 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, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover 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.\nResponsibilities:\nTHE ROLE:\nThe AMD AI Group is looking for a Senior Software Development Engineer to own the end-to-end model execution stack on AMD Instinct GPUs - spanning training infrastructure at scale and high-performance inference serving. This role demands someone who has shipped LLMs on real hardware, written GPU kernels that moved production metrics, and built the systems infrastructure (orchestration, storage, monitoring) that keeps thousands of GPUs productive. You will be instrumental in ensuring AMD GPUs are first-class citizens for frontier model training and inference across current and next-generation Instinct accelerators.\n\nKEY RESPONSIBILITIES:\nTraining Infrastructure & Enablement\nEnable and optimize large-scale model training (LLMs, VLMs, MoE architectures) on AMD Instinct GPU clusters, ensuring correctness, reproducibility, and competitive throughput.\nBuild and maintain training infrastructure: job orchestration, distributed checkpointing, data loading pipelines, and storage optimization for multi-thousand GPU clusters on Kubernetes.\nDebug and resolve training-specific issues including gradient norm explosions, non-deterministic behavior across GPU generations, and compute-communication overlap in distributed training (FSDP, DeepSpeed, Megatron-LM).\nOptimize RCCL collective communication patterns for training workloads, including all-reduce, all-gather, and reduce-scatter across multi-node topologies.\nDevelop monitoring, alerting, and compliance infrastructure to ensure training cluster health, data security, and SLA adherence at scale.\nInference Optimization & Serving\nWrite and optimize high-performance GPU kernels (GEMM, attention, quantized matmul, GPTQ/AWQ) in HIP, Triton, and MLIR targeting AMD Instinct architectures, with demonstrated ability to outperform open-source baselines.\nDrive end-to-end inference enablement on new AMD GPU silicon - be among the first to get frontier models running on each new Instinct generation, creating reproducible guides and reference implementations.\nOptimize inference serving frameworks (vLLM, SGLang, TorchServe) for AMD GPUs: batching strategies, KV-cache management, speculative decoding, and continuous batching for production throughput/latency targets.\nDevelop novel approaches to inference acceleration, including bio-inspired algorithms, SLM-assisted batching, and custom scheduling strategies that exploit AMD hardware characteristics.\nBuild quantization pipelines (FP8, FP6, FP4, GPTQ, AWQ) for production model deployment, ensuring quality-performance tradeoffs are well-characterized across AMD GPU generations.\nCross-Cutting\nDesign observability and debugging tooling: log analysis pipelines, anomaly detection systems, and failure correlation tools for large-scale GPU clusters processing hundreds of terabytes of telemetry per month.\nCollaborate with AMD silicon architecture teams to provide software feedback on next-generation Instinct GPU designs for both training and inference workloads.\nContribute to the open ROCm ecosystem and AMD’s developer experience - SDKs, CI dashboards, documentation, and developer cloud enablement.\nCollaborate closely with multiple teams to deliver key planning solutions and the technology to support them\nHelp contribute to the design and implementation of future architecture for a highly scalable, durable, and innovative system\nWork very closely with dev teams and Project Managers to drive results\n\nPREFERRED EXPERIENCE:\nStrong Industry experience shipping production AI/ML infrastructure, with hands-on work spanning both training and inference.\nProven experience running LLMs on AMD GPUs (ROCm, HIP) or equivalent depth with CUDA, with strong willingness to work on AMD platforms.\nTrack record of writing custom GPU kernels (CUDA, HIP, or Triton) that delivered measurable throughput improvements in production systems.\nStrong systems engineering skills: Kubernetes, container orchestration, distributed storage, and GPU cluster management at scale (1,000+ GPUs).\nProficiency in Python and at least one systems language (C++, Rust, Go, C#) with production-quality software engineering practices.\nDeep understanding of LLM architecture internals: attention mechanisms, KV-cache, quantization schemes, and distributed parallelism strategies (tensor, pipeline, expert parallelism).\nDirect experience enabling frontier models (GPT-4 class) on AMD Instinct hardware end-to-end.\nExpert knowledge and hands-on experience in C, C++\nSolid understanding of object-oriented-design principles\nSolid understanding of Software Engineering principles, Data structure, algorithms, Operating Systems concepts and multithread programming\nExcellent design and code development skills, familiarity with Linux and modern software tools and techniques for development\nGood analytical and problem-solving skills\n\nACADEMIC CREDENTIALS:\nBachelor’s or Master’s degree in Computer/Software Engineering, Computer Science, or related technical discipline\n\nThis role is not eligible for visa sponsorship.\n\n#LI-CJ3\n#LI-Hybrid\n\nQualifications:\nBenefits offered are described: AMD benefits at a glance.\n\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.\n\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.","datePosted":"2026-04-14T11:17:13.760Z","dateModified":"2026-04-14T11:17:13.760Z","hiringOrganization":{"@type":"Organization","name":"Advanced Micro Devices","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Santa Clara","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"67f5da5b1bff7fb64c7bda9e"},"url":"https://jobsearcher.com/jobs/67f5da5b1bff7fb64c7bda9e"}}