Lead AI Inference Performance Engineer - GPU & Scale

Advanced Micro Devices, Inc.; San Jose (CA); Presencial; USD 180.000 - 240.000; Tempo integral

Resumo da vaga

Advanced Micro Devices, Inc. is seeking a performance-minded engineer to push AI inference performance on AMD GPUs.

You will lead a small, highly technical team and optimize models end-to-end across the stack, from profiling to multi-node distributed inference, delivering measurable uplifts for strategic customer engagements. You will deeply understand GPU kernel behavior, profiling data, and hardware-software tradeoffs, and you will shape reusable methodologies and upstream contributions to

Qualificações

• 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. You may not write kernels from scratch daily, but you can tell exactly why one is slow and what needs to change
• Understanding of model architectures (transformers, MoE, diffusion), inference paradigms (speculative decoding, prefill-decode disaggregation, continuous batching), 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: ability to engage with customers, present findings, and drive decisions
• Fluent in AI-assisted development: daily user of AI agents and tools, with a mindset toward defining new AI-powered workflows
• Strong Linux systems knowledge
• Excellent written and verbal English communication skills

Responsabilidades

• 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

Conhecimentos

GPU computing

AI systems

Python

C++

Performance profiling

Kernel optimization

GPU architectures

Customer-facing leadership

English communication

Formação

Bachelor's/Master's/PhD in CS/CE/EE

Ferramentas

HIP

CUDA

Triton

CK

PyTorch

vLLM

SGLang

TensorRT-LLM

Gluon

Descrição da vaga

Advanced Micro Devices, Inc. is seeking a performance-minded engineer to push AI inference performance on AMD GPUs.

You will lead a small, highly technical team and optimize models end-to-end across the stack, from profiling to multi-node distributed inference, delivering measurable uplifts for strategic customer engagements. You will deeply understand GPU kernel behavior, profiling data, and hardware-software tradeoffs, and you will shape reusable methodologies and upstream contributions to