Senior Forward Deployed Engineer (FDE)

AMD; Medina; On-site; SAR 600,000 - 900,000; Full time

Job summary

AMD is seeking a Senior Member of Technical Staff for its Forward Deployed Engineering team in Medina. You will work directly with strategic customers to enable, optimize, and deploy GenAI inference workloads on AMD Instinct GPUs and CPUs.

You will bridge AI models with AMD hardware to deliver competitive performance across the inference stack. You will develop and tune GPU kernels, optimize pipelines, and collaborate with core teams to achieve targets while contributing to open-source projects

Qualifications

• 8+ years of relevant experience in GPU performance optimization
• Excellent knowledge of GPU architectures and programming models
• Hands-on experience with AMD ROCm (or CUDA) and kernel development
• Strong proficiency in Python and C++, with kernel development tools
• Experience with LLM inference frameworks and serving techniques

Responsibilities

• Partner directly with customers to enable and optimize LLMs and multimodal models on AMD Instinct GPUs
• Optimize GenAI inference pipelines and provide performance improvements
• Develop and tune low-level GPU kernels and tensor operations
• Profile workloads and identify bottlenecks across GPU-CPU boundaries
• Contribute optimizations to open-source AI frameworks and internal libraries

Skills

GPU architecture

Python

C++

Kernel development

Performance optimization

MoE architectures

Transformer models

Large language models

Education

BS, MS, or PhD in Computer Science or Electrical Engineering

Tools

Triton

HIP

CUDA

rocProfiler

ROCdebug-agent

Job description

About the Role

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

Responsibilities

• 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 strong AI-assisted coding tools and agentic workflows strong to accelerate development debugging optimization and code review across the inference stack

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 developer tooling
• Strong customer-facing communication skills and the ability to manage technical deliverables against committed timelines

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., 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).