Forward Deployed Engineer (FDE) for AI and GenAI Inference Optimization
RiyadhFull-timePosted 5d ago
About the role
Forward-facing technical role to enable, optimize, and deploy GenAI inference workloads on AMD data center CPUs/GPUs, bridging AI models and AMD hardware to achieve performance and accuracy targets.
Responsibilities
- Partner with customers as part of the FDE team 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.
Qualifications
- BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, along with 8+ years of relevant experience.
- Solid 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., GitHub 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., rocProfiler, ROCdebug-agent).
Skills
Required:Large language model (LLM) optimizationvLLM and SGLang frameworksGPU kernel developmentPython & C++AMD ROCm / CUDA
Benefits
- Benefits offered are described: AMD benefits at a glance.
Last updated Oct 22, 2026. Interested? Apply on the company site.
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