Technical Marketing Engineer – AI Training Workloads & Performance

Advanced Micro Devices, Inc•Santa Clara, CA

23d•Onsite

About The Position

AMD is committed to delivering exceptional AI training performance across the full model lifecycle. Ease of use, clarity, and strong technical enablement are core to how our customers succeed with AMD technologies. As a Technical Marketing Engineer (TME) within the Software Product Management organization for AMD’s Data Center GPU Business Unit, you will play a critical role in helping customers achieve optimal performance and efficiency when training and adapting models on AMD Instinct GPUs. In this role, you will bridge deep technical expertise with clear communication, helping customers, partners, and internal teams understand how AMD GPUs unlock performance and value across AI training workloads and industry benchmarks. Your work will ensure customers can get productive quickly, supported by high-quality documentation, performance insights, and hands-on examples that remove friction and accelerate adoption.

Requirements

BS in Computer Science, Computer Engineering, Electrical Engineering, or a related field required, MS or PhD is a plus.
5+ years optimizing AI training workloads on GPUs or accelerators at scale.
Hands-on experience with distributed training frameworks (e.g., PyTorch, DeepSpeed, Megatron-LM).
Strong programming skills in Python and/or C/C++.
Experience with ROCm, CUDA, or similar GPU compute stacks.
Experience working with ISVs, hyperscalers, or large-scale AI deployments.
Proven ability to create high-quality technical documentation and performance guides.
Proven experience delivering technical presentations, workshops, or participating in industry conferences, meetups, or hackathons.
Expertise in modern documentation and publishing workflows, including Markdown, Read the Docs, and Jupyter Notebooks.
Proficiency with standard productivity and publishing tools (e.g., Microsoft Office, Adobe Acrobat) used for technical documentation and training delivery.
Confidence presenting complex technical material in customer‑facing settings within a fast‑paced, large‑scale organization.

Nice To Haves

Background in solution validation, benchmarking, or certification frameworks is highly desirable.

Responsibilities

Partner with AMD’s AI software engineering team to develop performance-focused technical content for AI training workloads, including optimization guides, benchmarking results, scaling studies, and tuning methodologies.
Serve as a subject matter expert on AI training performance across the full model lifecycle, including: Large-scale pre-training (foundation models), Fine-tuning and parameter-efficient methods (e.g., LoRA, PEFT), Reinforcement learning workflows (e.g., RLHF, RLAIF), Distillation and model compression techniques, Quantization-aware training (QAT).
Develop and publish deep technical content for training workloads, including: Performance analysis and bottleneck breakdowns, Scaling studies (single-node and multi-node), Optimization guides for both pre-training and post-training workflows, Distributed training best practices (data/model/pipeline parallelism), Workload-specific tuning strategies and competitive positioning insights.
Analyze and optimize training performance across key system dimensions, including compute utilization, memory efficiency, communication overhead, and scaling behavior in distributed environments.
Engage with internal and external experts to validate performance claims against real-world scenarios and large-scale training runs.
Validate and analyze training performance results from internal benchmarks and customer proof-of-concept (POC) engagements, ensuring accuracy, reproducibility, and credibility.
Partner with engineering to translate low-level optimizations (kernels, communication patterns, memory usage) into actionable guidance and influence product improvements based on real-world workload feedback.
Stay current on industry training frameworks, distributed training strategies, and emerging techniques, identifying where AMD platforms deliver differentiated performance.
Act as a bridge between engineering, field teams (FAE/FDE), and business stakeholders, ensuring alignment on training performance capabilities, best practices, and messaging.
Design and deliver technical enablement (e.g., workshops, demos, technical sessions) to help internal teams and customers optimize AI training workloads.
Build deep empathy for the challenges faced by solutions architects and engineers deploying AMD GPUs, and create documentation that proactively removes barriers to adoption.
Actively gather and incorporate feedback from customers, field teams, and internal technical experts to continuously improve content quality and relevance.
Lead by example in documentation excellence, mentoring and influencing peers to raise the overall quality, consistency, and effectiveness of technical materials.
Communicate with clarity, integrity, and transparency across written, verbal, and visual formats.