Sr GenAI Infra Specialist SA, AWS WWSO Startup

About the role

You will be part of the core Specialist Organization focused on Startup Customers GenAI and Go-to-Market (GTM) team, focused on AI infrastructure for model training and inference optimization.

Responsibilities

• Work directly with the most important and exciting Startup customers in the GenAI model training and inference space, helping them adopt and scale large-scale workloads (e.g., frontier models, models, multi-modal systems, optimization) on AWS
• Advise customers on AI infrastructure requirements and trade-offs including GPU/Trainium selection, cluster topology, storage, networking (EFA), and cost optimization for training and inference
• Provide deep technical guidance on inference optimization model serving architectures (self-managed on EKS, SageMaker endpoints, Sagemaker Hyperpod Serving), batching strategies, quantization, model parallelism, and latency/throughput tradeoffs
• Provide deep technical guidance on training optimization distributed training strategies, framework selection (PyTorch, JAX, NeMo), SageMaker HyperPod, Slurm/PCS integration, checkpointing, and data pipeline design
• Guide customers on GPU and accelerator profiling identifying bottlenecks (compute, memory, I/O), optimizing utilization, and tuning system-level performance
• Help customers understand and apply model optimization techniques fine-tuning approaches (LoRA, QLoRA, full fine-tuning), RLHF/DPO, knowledge distillation, and efficient serving techniques (vLLM, TensorRT-LLM, Triton)
• Help Go-To-Market Specialist define and drive strategy on assets that impact growth through market sizing, building an opportunity pipeline, creating technical content to train field teams, and establishing thought leadership
• Develop demos, proof-of-concepts, reference architectures, and benchmarks that demonstrate AWS infrastructure value proposition for GenAI workloads
• Collaborate with product teams (EC2, Trainium/Inferentia, SageMaker, EKS, PCS, EC2) to shape product vision, prioritize features, and represent the voice of the customer
• Work with account teams, research scientists, ISVs, framework communities, and model providers to drive implementations and accelerate innovation

Qualifications

• Experience conveying complex technical concepts to both technical and business audiences
• 8+ years of experience in technology domain areas (e.g., systems engineering, cloud infrastructure, HPC, ML/AI, distributed computing)
• 3+ years of experience designing, implementing, or consulting on large-scale AI/ML infrastructure with hands-on experience on GPU-based computing, ML training infrastructure, and inference serving systems

Skills

• Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience with CUDA kernels or ML/low-level kernels
• Experience with vLLM, SGLang, TensorRT or similar platforms in production environments, or experience in performant kernel development (CUTLASS, FlashInfer)
• Experience with container orchestration for ML: EKS, Kubernetes operators for ML KubeRay, Karpenter, Keda, K8/DRA
• Experience with HPC schedulers and managed platforms: Slurm, AWS PCS (Parallel Computing Service), SageMaker HyperPod
• Experience with fine-tuning techniques: LoRA, QLoRA, RLHF, DPO, knowledge distillation, Quantization, KV optimization

Pay

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location.

USA, NY, New York - 169,000.00 - 228,600.00 USD annually

USA, VA, Herndon - 153,600.00 - 207,800.00 USD annually