Amazon
Software Development Engineer II, AWS SageMaker AI
$144K–$194K
BELLEVUE, WA +1 · Full-time · Posted Aug 5, 2026
- Location
- BELLEVUE, WA
Bellevue, WA - Experience
- 3+ years professional software development; 2+ years design or architecture; 1+ year software development engineer or related occupational experience; 1+ year large-scale software development using a listed language; 1+ year Object Oriented Design.
- Education
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field.
About the job
Build and operate Model Factory distributed systems that orchestrate foundation-model training and customization workflows at scale.
What you'll do
- Design and operate contract-validated workflows for foundation-model data preparation, training, evaluation, and deployment.
- Build compute integrations and improve resiliency, recovery, and observability for large-scale jobs.
- Contribute to the SDK, orchestration and contract layers, and platform infrastructure; own components from design through on-call operations.
What they ask for
- 3+ years of non-internship professional software development experience.
- 2+ years of non-internship design or architecture experience for new and existing systems, including design patterns, reliability, and scaling.
- At least 1 year designing and developing large-scale software applications or services using C#, C++, Java, or Perl; at least 1 year 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 language.
Nice to have
- 3+ years of full software development life cycle experience.
- Experience with workflow orchestration, container and cluster compute, cloud infrastructure-as-code, managed cloud services, or ML training workflows.
Pay
143,700.00 - 194,400.00 USD annually (base salary)
Tools
C#, C++, Java, Perl, SageMaker, EMR, AWS Batch, Kubernetes/EKS
AI focus
Build a platform for foundation-model training, customization, evaluation, and deployment; integrate containerized training and evaluation frameworks and large-scale accelerator compute.
Model-written summary · the original posting is authoritative