Post-Training Platform Infrastructure Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING 

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.  

THE ROLE:

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.  

THE PERSON:

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. 

KEY 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