Principal AI Performance Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING

At 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.

PRINCIPAL AI PERFORMANCE ENGINEER

THE ROLE:

AMD is looking for a performance-obsessed engineer to drive AI inference performance to the absolute limit on AMD GPUs. You will lead a small, highly technical team and work end-to-end across the stack: profiling, diagnosing, and optimizing leading models on customer-relevant serving configurations (e.g. agentic coding, long-context, high-throughput serving). You move from challenge to challenge, tackling the hardest performance problems across our most strategic customer engagements and leaving behind measurable uplifts and reusable methodology.

This is not a sustaining role: every engagement is different, every optimization leaves a lasting impact.

THE PERSON:

You can take any AI workload, understand it top to bottom, and make it faster. You are equally comfortable profiling a distributed serving deployment, diagnosing a kernel-level bottleneck, and presenting optimization results to a customer's VP of Engineering. You understand GPU kernel performance deeply: not just how to use profiling tools, but how to reason about occupancy, cache behavior, memory coalescing, and instruction-level bottlenecks from first principles.

You lead through technical depth: you set the standard for your team by doing the hardest work yourself and pulling others up along the way. You are AI-fluent, not just in the models you optimize, but in how you work: you leverage AI agents and tools daily to accelerate your workflows, and you actively define new ways of using them to make yourself and your team more effective.

You thrive under pressure, move fast, and measure everything.

KEY 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 Py Torch

PREFERRED EXPERIENCE:

* 7+ years of software development experience in GPU computing, AI systems, or high-performance computing

* Deep hands-on experience with AI serving frameworks (vLLM, SGLang, Tensor

RT-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…