Principal AI/ML HPC Specialist Technical Account Manager (STAM) , AWS Enterprise Support, NAMER-Sp

As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide Basic qualifications: - Bachelor's degree - 8+ years of experience in AI/ML, distributed computing, or GPU-accelerated infrastructure (e.g., model training, inference systems, HPC for ML) - 3+ years of hands-on experience designing, implementing, or consulting on large-scale ML training or inference architectures in a customer-facing role - 10+ years of IT development or implementation/consulting in the software, cloud computing, or AI/ML industries - Experience with at least one major deep learning framework (PyTorch, TensorFlow, JAX) in a production or research environment - Demonstrated ability to serve as a trusted technical advisor to enterprise customers Preferred: - Deep experience with distributed training techniques including data parallelism, model parallelism, pipeline parallelism, and Fully Sharded Data Parallel (PyTorch FSDP) - Experience with distributed training frameworks such as PyTorch DDP, DeepSpeed, and Megatron-LM for multi-node model training - Hands-on experience with GPU/accelerator cluster infrastructure: NVIDIA Blackwell (GB200, B200, B300), H100/H200 GPUs, AWS Trainium (Trn3/Trn2), NVLink/NVSwitch, InfiniBand or Elastic Fabric Adapter (EFA), and NCCL collective communications tuning - Experience with AWS Neuron SDK (torch-neuronx, neuronx-nemo-megatron) for compiling and optimizing models on Trainium and Inferentia (Inf2) instances - Familiarity with SageMaker HyperPod for managed distributed training clusters including automated health checks, node replacement, and checkpoint-based recovery - Experience with HPC job schedulers (Slurm, PBS, LSF) for orchestrating multi-node ML training workloads - Experience with high-performance parallel file systems (Amazon FSx for Lustre, GPFS/Spectrum Scale) for ML data pipelines - Familiarity with AWS Parallel Computing Service (PCS), AWS ParallelCluster, AWS Batch, or equivalent managed HPC/ML cluster services - Experience training or fine-tuning large language models (LLMs) such as Llama, GPT, or similar transformer architectures at multi-billion parameter scale - Understanding of HPC-AI convergence patterns: simulation-surrogate loops, physics-informed neural networks (PINNs), graph neural networks for molecular property prediction, and data format interoperability (HDF5, VTK, NetCDF to ML-ready tensors) - Knowledge of ML Ops tooling, container orchestration for training (Docker, Enroot, Pyxis), and Deep Learning AMIs (DLAMIs) - Experience with cluster observability and monitoring for GPU/Trainium utilization, training throughput, and job performance (CloudWatch, Prometheus, Grafana) - Experience with EC2 Capacity Blocks for ML, Capacity Reservations, or similar GPU capacity planning strategies - Experience with pipeline orchestration using AWS Step Functions for simulation-ML workflows - Experience with containers, EKS and ECS - Track record of driving operational excellence and proactive risk mitigation for mission-critical AI/ML workloads - AWS certifications (Solutions Architect Professional, Machine Learning Specialty) preferred Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. 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. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, TX, Austin - 182,800.00 - 247,300.00 USD annually USA, TX, Dallas - 182,800.00 - 247,300.00 USD annually USA, VA, Herndon - 182,800.00 - 247,300.00 USD annually USA, WA, Seattle - 182,800.00 - 247,300.00 USD annually