AI Performance Engineer

AMD is looking for a performance-obsessed senior engineer to push AI inference performance to the limit on AMD GPUs, with vLLM as the primary serving framework. You will work end-to-end across the stack: profiling, diagnosing, and optimizing leading models running on vLLM across customer-relevant serving configurations (e.g. agentic coding, long-context, high-throughput serving). You will own hard performance problems on our most strategic customer engagements and leave behind measurable uplifts and reusable methodology. KEY RESPONSIBILITIES: - Drive performance optimization end-to-end on vLLM across leading models and customer-relevant serving configurations, closing competitive gaps through kernel and systems-level optimizations - Profile, diagnose, and resolve cross-stack performance bottlenecks in vLLM deployments, from GPU kernels and operator dispatch to the vLLM scheduler, PagedAttention/KV cache management, 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 - Contribute to customer-facing technical engagements: present findings, recommend optimizations, and deliver measurable performance uplifts on vLLM - Integrate and optimize custom kernels (Triton, Gluon, CK, PyDSL, ASM, AITER) within vLLM, understanding dispatch paths, shape extraction, and backend selection - Optimize multi-node distributed inference on vLLM: communication-compute overlap, parallelism strategies (TP/PP/EP/DP), and scale-out performance - Contribute to shared performance optimization methodology that raises the bar across the team - Leverage AI agents to accelerate daily work and help define best practices for AI-assisted performance engineering - Upstream optimizations into vLLM and adjacent open-source frameworks such as SGLang and PyTorch PREFERRED EXPERIENCE: - 5+ years of software development experience in GPU computing, AI systems, or high-performance computing - Hands-on experience with vLLM internals (V1 engine, scheduler, PagedAttention/KV cache manager, chunked prefill, ROCm backend integration); familiarity with SGLang, 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 - 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 (speculative decoding, prefill-decode disaggregation, continuous batching), and how they map to hardware and to vLLM's execution model - 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 - Strong proficiency in Python and C++ - Ability to engage with customers, present findings, and support technical decisions - Fluent in AI-assisted development: daily user of AI agents and tools - 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. Advanced degree preferred but exceptional industry experience valued equally. LOCATION: Helsinki, Finland or Stockholm, Sweden.