Senior Forward Deployed Engineer; FDE) AI and GenAI Inference Optimization

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Senior Forward Deployed Engineer (FDE) for AI and GenAI Inference Optimization

We are seeking a Senior Member of Technical Staff to join our Forward Deployed Engineering (FDE) team. In this customer-facing role, you will work directly with strategic partners and customers to enable, optimize, and deploy state-of-the-art GenAI inference workloads on AMD data center CPUs and GPUs (Instinct™ family). You will bridge the gap between cutting‑edge AI models and AMD hardware, driving competitive performance across the full inference stack.

• Partner directly with customers to enable and optimize large language models (LLMs) and multimodal models on AMD Instinct GPUs, meeting committed performance and accuracy targets.
• Optimize GenAI inference pipelines on vLLM and SGLang, including PD disaggregation, expert parallelism (EP/wide-EP), tensor/data parallelism, speculative decoding, and KV cache management.
• Develop and tune low-level GPU kernels (Triton, HIP, composable kernels) for attention, MoE, GEMM, quantization, and normalization operations.
• Profile end-to-end workloads, identify bottlenecks across GPU/CPU boundaries, and deliver measurable throughput and latency improvements versus competitive targets.
• Enable advanced data types and techniques (FP8, MXFP4, sparse attention) with a focus on both performance and numerical accuracy.
• Debug complex runtime, stability, and accuracy issues in production environments; partner with core engineering teams to identify root causes and contribute fixes.
• Contribute optimizations back to open‑source frameworks (vLLM, SGLang, ATOM, AITER) and internal libraries; document best practices and deliver production‑ready artifacts to customers.
• Leverage AI-assisted coding tools and agentic workflows to accelerate development, debugging, optimization, and code review across the inference stack.

• BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, along with 8+ years of relevant experience.
• Deep expertise in GPU architecture and performance optimization, including hands‑on experience with AMD ROCm (or CUDA) and GPU kernel development.
• Strong proficiency in Python and C++, with experience developing and optimizing kernels using Triton, HIP, or CUDA.
• Experience with AI-assisted coding and agentic development tools (e.g., Git Hub Copilot, Claude, Cursor, or equivalent) to enhance productivity in kernel development, large‑scale codebase navigation, debugging, and framework integration.
• Experience working with LLM inference frameworks (vLLM, SGLang, or equivalent) and serving optimization techniques.
• Solid understanding of transformer and MoE architectures, quantization, and distributed inference strategies (TP, DP, EP, and related parallelism techniques).
• Proficiency with profiling and debugging tools (e.g., roc Profiler, ROCdebug-agent).

• Experience optimizing frontier models (Deep Seek, Qwen, Kimi, Mini Max, GLM, etc.) in production environments.
• Track record of contributing to open‑source AI inference frameworks.
• Experience integrating AI‑driven automation into engineering workflows and developer tooling.
• Strong customer‑facing communication skills and the ability to manage…