AI Performance Engineer

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). KEY RESPONSIBILITIES: - 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 - Upstream optimizations into SGLang and adjacent open-source frameworks such as vLLM and PyTorch PREFERRED EXPERIENCE: - 7+ years of software development experience in GPU computing, AI systems, or high-performance computing - Deep hands-on experience with SGLang internals; familiarity with vLLM, TensorRT-LLM, or similar is a plus - Strong background in end-to-end workload profiling and bottleneck diagnosis - Understanding of GPU kernel performance characteristics: occupancy, register and LDS pressure, memory coalescing, cache utilization, wavefront scheduling, and instruction-level bottlenecks - Understanding of model architectures (transformers, MoE, diffusion), inference paradigms (speculative decoding, prefill-decode disaggregation, continuous batching) - Experience with custom kernel development or integration (HIP, CUDA, Triton, CK, or similar) - Understanding of multi-GPU and multi-node distributed systems: RCCL/NCCL, RDMA - Strong proficiency in Python and C++ - Customer-facing technical leadership experience - Fluent in AI-assisted development - Strong Linux systems knowledge - Excellent written and verbal English communication skills ACADEMIC CREDENTIALS: Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.