Principal AI Performance Engineer - LLM Inference (SGLang)

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.

AMD is looking for a performance-obsessed engineer to drive AI inference performance to the absolute limit on AMD GPUs, with SGLang as the primary serving framework. You will lead a small, highly technical team and work end-to-end across the stack: profiling, diagnosing, and optimizing leading models running on SGLang across 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 on SGLang. You know the framework's internals intimately: RadixAttention and prefix caching, the scheduler and continuous batching loop, the SGLang runtime and its interaction with the AMD backend, and the paths that connect a user request down to the GPU kernel. You are equally comfortable profiling a distributed SGLang 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.

Drive performance optimization end-to-end on SGLang across 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 in SGLang deployments, from GPU kernels and operator dispatch to the SGLang scheduler, RadixAttention/prefix caching, 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 on SGLang Integrate and optimize custom kernels (Triton, Gluon, CK, PyDSL, ASM, AITER) within SGLang, understanding dispatch paths, shape extraction, and backend selection Optimize multi-node distributed inference on SGLang: communication-compute overlap, parallelism strategies (TP/EP/DP), 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