SageMaker AI: Scalable ML Training Orchestrator

Amazon

Factoria (WA)

On-site

USD 144,000 - 194,000

Full time

14 days+

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Job summary

Amazon in Bellevue, WA is seeking a Software Development Engineer II for AWS SageMaker AI to design, build, and operate the orchestration engine, compute integrations, and platform services for large-scale model training and customization. You’ll own end-to-end components from design to on-call operations while collaborating with ML scientists and platform teams.

You will contribute to the SDK, workflow orchestration, and containerized training frameworks, upholding high engineering standards

Qualifications

3+ years of non-internship professional software development experience.
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
1+ years of software development engineer or related occupational experience
1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
1+ years of Object Oriented Design experience
Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
Experience programming with at least one software programming language

Responsibilities

Design, build, test, and operate services that orchestrate foundation-model data preparation, training, evaluation, and deployment as reliable, contract-validated workflows.
Own delivery of individual components end-to-end — from design and implementation through deployment, monitoring, and on-call operations.
Build and extend compute-backend integrations and job launchers — submitting, monitoring, and recovering large-scale training jobs across SageMaker (Training/Processing/HyperPod), EMR, AWS Batch, and Kubernetes/EKS.
Improve the platform's resiliency and operability for long-running distributed jobs — checkpoint/resume, fault detection and recovery, retries, and observability (metrics, logging, experiment tracking).
Contribute to the SDK, workflow orchestration, and schema/contract layer that teams use to declare and run jobs, and to the CDK infrastructure that deploys the platform.
Integrate containerized training and evaluation frameworks (e.g., PyTorch/FSDP, verl, NeMo/Megatron) into the platform's task and recipe model.
Contribute to design and architecture discussions, write clear technical designs, and uphold engineering best practices (code review, testing, operational readiness).
Collaborate with ML scientists and internal customers to translate training requirements into reliable, self-service platform capabilities, and help onboard and mentor interns and new engineers as you grow.