layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Post-Training Platform Infrastructure Engineer at AMD | LAYIQ

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
PREFERRED EXPERIENCE: 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
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
ACADEMIC CREDENTIALS: Bachelor’s or master's degree in computer science
, computer engineering, electrical engineering, or equivalent
LOCATION: San Jose, CA (Hybrid). May consider other US locations.
#HYBRID

Responsibilities

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
Document findings, architectural insights, and best practices to guide future system design
PREFERRED EXPERIENCE: 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
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
ACADEMIC CREDENTIALS: Bachelor’s or master's degree in computer science
, computer engineering, electrical engineering, or equivalent
LOCATION: San Jose, CA (Hybrid). May consider other US locations.
#HYBRID

Post-Training Platform Infrastructure Engineer

Employer job description

WHAT YOU DO AT AMD CHANGES EVERYTHING

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