← All roles

Advanced AI Workloads - Performance and Scalability Engineer

AMD· Engineering
Apply on company site
Hybrid (San Jose, CA)$240k - $360kFull-timePosted 6mo ago

About the role

Principal Engineer leading a hands-on technical team to drive performance and scalability of frontier AI workloads on AMD GPUs, including large language models, mixture-of-experts architectures, and diffusion models. Define long-term technical vision, tackle cross-stack performance challenges, and influence open-source frameworks and architecture.

Responsibilities

  • Lead a small team of engineers: set technical direction, prioritize work, and ensure delivery while remaining deeply hands-on
  • Define and drive the long-term technical strategy for AI workload performance on AMD GPUs
  • Own the most complex cross-stack performance challenges, from kernel optimization to framework-level architecture decisions
  • Lead the design and implementation of novel GPU kernels, compiler optimizations, and framework features
  • Establish performance methodology and roofline analysis practices that set the standard for the team
  • Influence upstream roadmaps in major open-source AI frameworks (e.g., vLLM, SGLang, PyTorch)
  • Drive architecture decisions for emerging inference paradigms (e.g., prefill-decode disaggregation, speculative decoding, distributed serving)
  • Identify and close fundamental performance gaps between AMD and competitor platforms
  • Serve as a technical authority across the organization, advising leadership on technical direction and feasibility
  • Mentor engineers and raise the technical bar across the broader engineering organization
  • Represent AMD externally through publications, conference talks, and open-source contributions

Qualifications

  • Deep software development experience in GPU computing, HPC, or AI systems
  • Deep understanding of GPU micro-architecture, memory hierarchy, instruction scheduling, and performance tradeoffs
  • Deep understanding of end-to-end AI systems: model architectures, inference paradigms, and system/rack-level design
  • Understanding of multi-GPU communication: scale-up (NVLink, xGMI, Infinity Fabric) and scale-out (RDMA, RCCL/NCCL) topologies and performance characteristics
  • Experience designing and optimizing across the full stack: from low-level GPU kernels to frameworks and distributed serving systems
  • Strong background in performance engineering, including profiling, roofline analysis, and bottleneck diagnosis at scale
  • Experience with one or more of: HIP, CUDA, OpenCL, Triton/Gluon, CUTLASS, CK
  • Experience with GPU compiler toolchains (e.g., LLVM) and intermediate representations (e.g., MLIR, LLVM IR, Triton IR) is a plus
  • Hands-on experience contributing to or architecting major open-source AI frameworks (e.g., vLLM, SGLang, xDiT, Megatron LM, PyTorch)
  • Strong proficiency in C++ (C++17 or later) and Python
  • Experience leading small technical teams while remaining a hands-on contributor
  • Track record of influencing technical direction across teams and organizations
  • Strong Linux systems knowledge
  • Excellent written and verbal English communication skills
  • Published research or significant open-source contributions in GPU computing, HPC, or AI systems is a plus

Skills

Required:GPU programmingGPU micro-architectureEnd-to-end AI systemsMulti-GPU communicationFull-stack optimizationPerformance engineeringProgramming languages

Benefits

  • AMD benefits at a glance (link provided in posting)
Last updated Oct 15, 2026. Interested? Apply on the company site.
AMD logo

About AMD

together we advance_AI

AMD delivers leadership high-performance and adaptive computing solutions to advance data center AI, AI PCs, intelligent edge devices, gaming, & beyond.

AIData CenterHigh-Performance ComputingGamingEdge Devices
Company profileWebsite

More open roles at AMD