Performance Engineering
About The Position
As a Performance Engineer, you will spearhead the next generation of AI infrastructure by defining GPU architecture specifications that enable massive model training at scale. Your expertise will drive 2-3x performance gains in both training and inference pipelines through innovative system design and optimization. You will champion the adoption of cutting-edge techniques across the engineering organization, from efficient attention mechanisms to advanced parallelization strategies. By establishing comprehensive best practices for distributed ML systems, you will create a framework that enables seamless scaling from single-GPU to thousand-GPU deployments.
Requirements
- Extensive and Senior experience optimizing large-scale ML systems and GPU architectures
- Deep expertise in CUDA programming, GPU memory hierarchies, and hardware-specific optimizations
- Proven track record architecting distributed training systems handling large scale systems
- Expert knowledge of transformer architectures, attention mechanisms, and model parallelism techniques
- PyTorch, CUDA, TensorRT, OpenAI Triton
- Distributed systems: Ray, Megatron-LM
- Performance analysis tools: NSight Compute, nvprof, PyTorch Profiler
- KV cache optimization, Flash Attention, Mixture of Experts
- High-speed networking: InfiniBand, RDMA, NVLink
- Bachelors, MS/PhD in Computer Science/Engineering or equivalent industry experience
Responsibilities
- Lead performance modeling and optimization for multi-trillion parameter LLM training/inference including Dense, Mixture of Experts (MoE) with multiple modalities (text, vision, speech)
- Model/optimize novel parallelization strategies across tensor, pipeline, context, expert and data parallel dimensions
- Architect memory-efficient training systems utilizing techniques like structured pruning, quantization (MX formats), continuous batching/chunked prefill, speculative decoding
- Incorporate and extend SOTA models such as GPT-4, Reasoning models (Deepseek-R1), and multi-modal architectures
- Collaborate with internal and external stakeholders/ML researchers to disseminate results and iterate at rapid pace.
Benefits
- AMD benefits at a glance.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
