Post-Training Platform Infrastructure Engineer

Advanced Micro Devices, Inc • San Jose, CA

5h • Hybrid

About The Position

At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. We are looking for a systems-minded engineer who lives at the intersection of large-scale model inference, distributed systems, and performance optimization. This role focuses on post-training and inference infrastructure, with particular emphasis on P/D disaggregation, KV cache lifecycle management, and efficient offloading mechanisms across both inference and reinforcement learning (RL) systems. You enjoy reverse-engineering modern ML infrastructure, reasoning about memory and compute tradeoffs, and turning research insights into production-grade features. You are comfortable diving into unfamiliar frameworks, understanding their architectural choices, identifying bottlenecks, and improving them through principled engineering.

Requirements

• Strong background in systems engineering, distributed systems, or ML infrastructure
• Hands-on experience with GPU-accelerated workloads and memory-constrained systems
• Solid understanding of:
• LLM inference workflows (prefill vs decode)
• Attention mechanisms and KV cache behavior
• Multi-process / multi-GPU execution models
• Proficiency in Python and C++ (or similar systems languages)
• Experience debugging performance issues using profiling tools (GPU, CPU, memory)
• Ability to read, understand, and modify complex open-source codebases
• Strong analytical skills and comfort working in research-heavy, ambiguous problem spaces

Nice To Haves

• Direct experience with LLM inference frameworks or serving stacks
• Familiarity with:
• GPU memory hierarchies (HBM, pinned memory, NUMA considerations)
• KV cache compression, paging, or eviction strategies
• Storage-backed offloading (NVMe, object stores, distributed file system)
• Experience with distributed RL or post-training pipelines
• Knowledge of scheduling systems, async execution, or actor-based runtimes
• Contributions to open-source ML or systems projects
• Experience designing benchmarking suites or performance evaluation frameworks

Responsibilities

• Research and deeply understand modern LLM inference frameworks, including:
• Architecture and design tradeoffs of P/D (prefill / decode) disaggregation
• KV cache lifecycle, memory layout, eviction strategies, and reuse
• KV cache offloading mechanisms across GPU, CPU, and storage backends
• Analyze and compare inference execution paths to identify:
• Performance bottlenecks (latency, throughput, memory pressure)
• Inefficiencies in scheduling, cache management, and resource utilization
• Develop and implement infrastructure-level features to:
• Improve inference latency, throughput, and memory efficiency
• Optimize KV cache management and offloading strategies
• Enhance scalability across multi-GPU and multi-node deployments
• Apply the same research-driven approach to RL frameworks:
• Study post-training and RL systems (e.g., policy rollout, inference-heavy loops)
• Debug performance and correctness issues in distributed RL pipelines
• Optimize inference, rollout efficiency, and memory usage during training
• Collaborate with research and applied ML teams to:
• Translate model-level requirements into infrastructure capabilities
• Validate performance gains with benchmarks and real workloads
• Document findings, architectural insights, and best practices to guide future system design

Benefits

• AMD benefits at a glance.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level