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Advanced AI Workloads - Performance and Scalability Engineer

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

About the role

Performance-focused engineer to drive AI inference performance on AMD GPUs, leading a small technical team across profiling, diagnosing, and optimizing models in customer-relevant serving configurations. Not a sustaining role; engagements vary with measurable uplifts and reusable methodology.

Responsibilities

  • Drive performance optimization end-to-end across the stack on leading models and customer-relevant serving configurations, closing competitive gaps through kernel and systems-level optimizations
  • Profile, diagnose, and resolve the hardest cross-stack performance bottlenecks, from GPU kernels and operator dispatch to framework-level scheduling and multi-node communication
  • Diagnose kernel-level performance issues using profiling tools: identify occupancy limitations, L2 cache thrashing, register pressure, memory coalescing issues, etc, and translate findings into actionable optimizations
  • Lead customer-facing technical engagements: present findings, recommend optimizations, and deliver measurable performance uplifts
  • Integrate and optimize custom kernels (Triton, Gluon, CK, PyDSL, ASM, AITER) within serving frameworks, understanding dispatch paths, shape extraction, and backend selection
  • Optimize multi-node distributed inference: communication-compute overlap, parallelism strategies, and scale-out performance
  • Develop and refine shared performance optimization methodology that raises the bar across the broader team
  • Leverage AI agents to accelerate daily work and define best practices for AI-assisted performance engineering
  • Upstream optimizations into open-source frameworks such as vLLM, SGLang, and PyTorch

Qualifications

  • Software development experience in GPU computing, AI systems, or high-performance computing
  • Deep hands-on experience with AI serving frameworks (vLLM, SGLang, TensorRT-LLM, or similar) and their internals
  • Strong background in end-to-end workload profiling and bottleneck diagnosis: you can trace from user request to GPU kernel and back
  • Understanding of GPU kernel performance characteristics: occupancy, register and LDS pressure, memory coalescing, cache utilization, wavefront scheduling, and instruction-level bottlenecks
  • Ability to read and reason about kernel-level profiling data and translate it into concrete optimization actions
  • Understanding of model architectures (transformers, MoE, diffusion), inference paradigms, and how they map to hardware
  • Experience with custom kernel development or integration (HIP, CUDA, Triton, CK, or similar)
  • Understanding of multi-GPU and multi-node distributed systems: scale-up and scale-out topologies, RCCL/NCCL, RDMA, and communication-compute overlap
  • System and rack-level design awareness: understanding performance tradeoffs across the full deployment stack
  • Strong proficiency in Python and C++
  • Customer-facing technical leadership experience
  • Fluent in AI-assisted development and daily use of AI agents/tools
  • Strong Linux systems knowledge
  • Excellent written and verbal English communication skills

Skills

Required:GPU performance profilingKernel optimizationAI serving frameworksPythonC++Linux
Nice to have:Distributed systems

Benefits

  • Benefits offered are described: AMD benefits at a glance
Last updated Oct 15, 2026. Interested? Apply on the company site.
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