Principal AI/ML HPC TAM - Enterprise-Scale GPU Training

Amazon; Austin (TX); Presencial; USD 183.000 - 247.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

RSUs

Health insurance

401(k) matching

Sign-on bonus

Descripción de la vacante

Amazon Web Services (AWS) seeks a Principal AI/ML HPC Specialist to join the TAM team in guiding NAMER enterprise customers through advanced AI/ML transformations. You will architect and optimize distributed training clusters, leverage SageMaker HyperPod and Slurm, and drive performance and cost governance at scale.

You will partner with TAMs, SAs, and service teams to deliver best-practice guidance on HPC-to-AI convergence, GPU/Trainium utilization, and end-to-end ML workflows.

Formación

• Bachelor's degree required.
• 8+ years in AI/ML, distributed computing, or GPU-accelerated infrastructure.
• 3+ years designing or consulting on large-scale ML training/inference architectures in a customer-facing role.
• 10+ years IT development/implementation in software/cloud/AI domains.
• Experience with major DL frameworks (PyTorch, TensorFlow, JAX).
• Proven ability to advise enterprise customers on AI/ML infra.

Responsabilidades

• Lead technical deep-dives and performance optimization for enterprise AI/ML workloads.
• Design and implement production-grade AI/ML training and inference solutions.
• Support customers in HPC capabilities and distributed data processing patterns.
• Improve training throughput and GPU/Trainium utilization across multi-node clusters.
• Build trusted advisor relationships with enterprise stakeholders.

Conocimientos

AI/ML experience

Distributed computing

GPU-accelerated infra

Customer-facing

Educación

Bachelor's degree

Herramientas

PyTorch

TensorFlow

JAX

SageMaker HyperPod

Slurm

NCCL

Descripción del empleo

Amazon Web Services (AWS) seeks a Principal AI/ML HPC Specialist to join the TAM team in guiding NAMER enterprise customers through advanced AI/ML transformations. You will architect and optimize distributed training clusters, leverage SageMaker HyperPod and Slurm, and drive performance and cost governance at scale.

You will partner with TAMs, SAs, and service teams to deliver best-practice guidance on HPC-to-AI convergence, GPU/Trainium utilization, and end-to-end ML workflows.