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

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset.

Are you ready to transform how businesses leverage artificial intelligence and machine learning at scale? Join our team and become a strategic partner in delivering Amazon AI/ML solutions that empower global enterprises to innovate, optimize, and achieve unprecedented operational excellence.

Amazon Web Services (AWS) is seeking an experienced Principal AI/ML HPC Specialist to join our Technical Account Manager (TAM) team.

You'll be at the forefront of solving complex AI HPC implementation challenges, guiding NAMER Research labs to enterprise customers through their most ambitious machine learning transformation journeys. By combining deep technical expertise with collaborative problem-solving, you'll help organizations unlock the full potential of artificial intelligence and machine learning technologies - from distributed model training on GPU clusters to production-grade inference at scale.

AWS Support includes experts from across AWS who help our customers design, build, operate, and secure their cloud environments. Customers innovate with AWS Professional Services, upskill with AWS Training and Certification, optimize with AWS Support and Managed Services, and meet objectives with AWS Security Assurance Services. Our expertise and emerging technologies include AWS Partners, AWS Sovereign Cloud, AWS International Product, and AI/ML-native solutions. You'll join a diverse team of technical experts in dozens of countries who help customers achieve more with the AWS cloud.

Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

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• 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
• 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

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

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• Deliver Strategic Technical Engagements – Lead comprehensive technical deep-dives and performance optimization for enterprise AI/ML workloads, including distributed training cluster architecture using AWS Parallel Computing Service (PCS) and AWS ParallelCluster, the latest GPU-accelerated computing (i.e., P6/P6e, G7/G7e instances), AWS Trainium-based training (Trn3 UltraServers), and multi-node NCCL communication tuning over EFA's SRD protocol.
• Architect and Validate Innovative Solutions - Design and implement production-grade AI/ML training and inference solutions leveraging Slurm-based job scheduling, distributed training frameworks (PyTorch FSDP, DDP, DeepSpeed, Megatron-LM), SageMaker HyperPod for managed GPU clusters with automated health checks and node replacement, high-performance parallel storage (Amazon FSx for Lustre), and container runtimes on Deep Learning AMIs (DLAMIs) against reference architectures and HPC lens to ensure performance, reliability, and cost governance at scale. Architect solutions using P6e UltraServers for multi-trillion parameter frontier models and Trn3 with the AWS Neuron SDK for cost-optimized training and inference.
• Enable Customer Success – Support customers in implementing business-critical HPC capabilities, including the development of large language model (LLM) (Llama, GPT-class models), physics-informed neural networks (PINNs) and surrogate models, MLOps pipelines, simulation-ML hybrid architectures orchestrated by AWS Step Functions and AWS Batch, distributed data processing, cluster observability, and governance controls for GPU/Trainium-intensive workloads.
• Enable Business Critical Outcomes – Partner with service teams to enhance model training throughput, optimize NCCL collective communications, improve GPU/Trainium utilization across multi-node UltraClusters, and drive operational efficiency through proactive monitoring, automated failure recovery (HyperPod health checks), and capacity planning (EC2 Capacity Blocks for ML). Contribute to product roadmap and feature requests, share reference architecture performance and benchmarks with broader TAM and Technical communities.
• Serve as Trusted Advisor and Advocate – Develop and nurture technical partnerships with enterprise stakeholders, serving as the trusted advisor for AI/ML infrastructure decisions spanning compute, networking (Elastic Fabric Adapter with SRD), storage, orchestration, and the HPC-to-AI convergence journey.

A day in the life

Your day will be dynamic and impactful involving deep technical consultations on distributed training architectures strategic solution design for GPU and Trainium cluster deployments and collaborative problem-solving across multi-node ML environments. You'll engage with technical leaders architect innovative AI/ML implementations - from Slurm-managed PCS clusters and SageMaker HyperPod to PyTorch FSDP/DeepSpeed training jobs and Neuron SDK compilation workflows - and provide expert guidance that bridges machine learning infrastructure with business objectives.