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

Advanced Micro Devices, Inc.; Riyadh; On-site; SAR 550,000 - 750,000; Full time

Job summary

Advanced Micro Devices, Inc. seeks a Forward Deployed Engineer to join the FDE team.

You will work directly with strategic partners to enable and optimize GenAI inference workloads on AMD data center CPUs and GPUs, bridging AI models and hardware for peak performance. In this customer-facing role, you will profile workloads, implement kernel optimizations, and contribute to open-source frameworks while leveraging AI-assisted coding tools to accelerate development and debugging across the

Qualifications

• BS/MS/PhD in CS or EEE with 8+ years of relevant experience.
• Expert in GPU architecture and performance optimization.
• Experience with AMD ROCm (or CUDA) and GPU kernel development.
• Proficient in Python and C++ for kernel development.
• Experience with AI-assisted coding tools and LLM inference frameworks.

Responsibilities

• Partner with customers as part of the FDE team to enable and optimize GenAI inference workloads on AMD Instinct GPUs, meeting performance and accuracy targets.
• Develop and tune low-level GPU kernels (Triton, HIP, CUDA) for attention, MoE, GEMM, quantization, and normalization operations.
• Profile end-to-end workloads, identify bottlenecks across GPU/CPU boundaries, and deliver throughput and latency improvements.
• Debug complex runtime, stability, and accuracy issues in production environments; collaborate with core engineering teams to fix root causes.
• Contribute optimizations back to open-source frameworks (vLLM, SGLang, ATOM, AITER) and internal libraries; produce production-ready artifacts.
• Leverage AI-assisted coding tools and agentic workflows to accelerate development, debugging, optimization, and code review across the inference stack.

Skills

GPU architecture

Performance optimization

Python

C++

Kernel development

AI-assisted coding tools

LLM inference frameworks

Transformer & MoE architectures

Profiling tools

Distributed inference

Education

BS/MS/PhD in CS or EEE

Tools

Triton

HIP

CUDA

rocProfiler

ROCdebug-agent

Job description

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believetechnology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMDis shapingthefuture.

Whetheryou’redesigning next-gen processors, enabling AI breakthroughs, orbringing leading edge products to market, every role at AMD contributes to something bigger— technologythat moves the world forward.Join us and, together, we’ll advance your career.

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

About the Role

We are seeking a 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 as part of the FDE team 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.

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.

What You'll Bring

• 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).

Nice to Have

• Experience optimizing frontier models (DeepSeek, Qwen, Kimi, MiniMax, GLM, etc.) in production environments.
• Track record of contributing to open-source AI inference frameworks.
• Experience integrating AI-driven automation into engineering workflows and