Description

Want to help drive the success of Machine Learning technologies at AWS? Do you have the skills and motivation to own the infrastructure that enables peer teams to ship high-quality ML networking software? We want to talk to you!

We seek a Senior Software Development Engineer for the Machine Learning (ML) Infrastructure team to own and evolve the platforms used to guarantee top performance of AWS ML and High Performance Computing (HPC) technologies developed by our organization. Our performance data directly influences launch decisions for new EC2 instance types and has visibility at the highest levels of the company. Bring your exceptional knowledge of CI/CD automation, cluster management, and ML/HPC workloads to bear on the cutting-edge software we develop. Join us as we expand the AWS offerings for AI, including Trainium, Neuron and the Elastic Fabric Adapter (EFA).

Key job responsibilities
Own the infrastructure that monitors and reports on functionality and performance of massive testing workloads run at scale across multiple GPU instance types. Build and operate CI/CD systems using Jenkins, internal Amazon tools, Linux, and public AWS products to automate the testing and delivery of ML networking libraries — including collective communication frameworks, network transport layers, and GPU communication libraries. Write Python code that orchestrates large clusters, runs benchmarks and ML applications across a wide matrix of instance types, operating systems, and software stack versions. Use AWS Managed Grafana and Athena to digest the massive amount of performance data generated by these workloads and build dashboards and alarms that catch functional and performance regressions before they reach customers. Build intelligent automation using LLMs to analyze test failures, perform root cause analysis, deduplicate regressions, and generate reports — reducing manual toil and accelerating issue resolution. Drive cross-team readiness for new instance type launches by delivering the performance data that shapes go/no-go decisions. Manage GPU compute capacity planning and provisioning across the organization. Own the complexity of infrastructure that covers many instance types, software stacks, Linux operating systems, and cutting-edge releases — and make it easy to evolve.

A day in the life
You write Python to orchestrate test workloads across large GPU clusters and TypeScript with CDK to ensure all infrastructure is code, reviewed and committed to automated pipelines. You manage shared development clusters using SLURM and AWS ParallelCluster, supporting multiple teams of developers while keeping costs down. You build AI-powered automation that analyzes nightly test results and surfaces actionable insights to developers. You write crisp designs for your projects, drive cross-team alignment, and make architectural decisions that affect how the organization tests and releases software.

About the team
We are part of Annapurna Labs, a subsidiary in AWS that builds software and hardware that make ML on EC2 work. Our organization is a dedicated group of innovators that have invented new networks, new silicon, new software suites, and combined those to entice customers to move immense ML and HPC workloads to the cloud. The ML Infrastructure team is laser focused on making AWS the best and most cost-effective place for customers to do AI at scale. Our work directly enables the launch of new GPU instance types — the performance data we produce reaches senior leadership and shapes strategic decisions.