Senior AI/ML Technical Account Manager

Amazon Web Services (AWS); San Francisco (CA); On-site; USD 210,000 - 284,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Web Services, Inc. in San Francisco seeks a Senior Technical Account Manager for Frontier AI to lead strategic technical engagements in AI/ML workloads, including distributed training across GPU clusters and production-scale inference, guiding customers through complex architectures and ensuring operational health.

You will partner with Solution Architects and service teams to implement best practices in PCS, ParallelCluster, SageMaker HyperPod, and NCCL optimizations, travel up to 10%

Qualifications

• Bachelor's degree in Computer Science, Math, or related discipline.
• 7+ years of design/implementation/operations/consulting with distributed applications experience.
• 7+ years of technical engineering experience.

Responsibilities

• Deliver Strategic Technical Engagements — Lead technical deep-dives for enterprise AI/ML workloads and performance optimization.
• Architect and Validate Innovative Solutions — Support production-grade AI/ML training and inference using Slurm, PyTorch FSDP, DeepSpeed, and SageMaker HyperPod.
• Enable Customer Success — Help customers deploy HPC capabilities, LLMs, MLOps pipelines, and governance for GPU/Trainium workloads.
• Enable Business Critical Outcomes — Improve training throughput, NCCL performance, and cost governance at scale.
• Serve as Trusted Advisor and Advocate — Partner with stakeholders to guide compute, networking, storage, and HPC-to-AI convergence.

Skills

Distributed computing

Technical engineering

Customer-facing lead

Education

Bachelor's degree in CS/Math or related

Tools

SageMaker HyperPod

Slurm

PBS

LSF

EKS

NCCL

PCS/ParallelCluster

Batch

Job description

Amazon Web Services, Inc. in San Francisco seeks a Senior Technical Account Manager for Frontier AI to lead strategic technical engagements in AI/ML workloads, including distributed training across GPU clusters and production-scale inference, guiding customers through complex architectures and ensuring operational health.

You will partner with Solution Architects and service teams to implement best practices in PCS, ParallelCluster, SageMaker HyperPod, and NCCL optimizations, travel up to 10%