Software Development Engineer II, AWS SageMaker AI

At AWS SageMaker AI were making it easy to build state-of-the-art foundation models on the cloud. Model Factory is our platform for building training customizing and evaluating foundation models at scale. Instead of hand-chaining data prep distributed training evaluation and deployment across thousands of GPU and AWS Trainium devices Model Factory lets teams express the whole lifecycle as a single contract-validated workflow orchestrated reproducible and fully managed. As LLMs and Generative AI scale Model Factory is the platform that turns frontier training research into a reliable repeatable pipeline for our internal teams and customers.

Were looking for a Software Development Engineer to help design build and operate the distributed systems at the core of this platform the orchestration engine compute integrations SDK and contract layer and the infrastructure that runs large-scale training and customization jobs. Youll own components end-to-end from design through delivery and on-call operations and work closely with the ML scientists and platform teams who depend on Model Factory every day. Youll turn requirements into robust scalable supportable services that fit cleanly into the overall architecture uphold a high engineering bar and grow your scope and technical leadership as you go.

A successful candidate has a strong software-engineering foundation writes high-quality distributed-systems and services code communicates clearly and is motivated to deliver results in a fast-paced ambiguous environment.

Key job responsibilities
As a Software Development Engineer on the SageMaker AI team you will:
- 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 platforms 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 platforms 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.

- 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
- Bachelors degree or foreign equivalent in Computer Science Engineering Mathematics or a related field
- Experience programming with at least one software programming language

- 3 years of full software development life cycle including coding standards code reviews source control management build processes testing and operations experience
- Bachelors degree in computer science or equivalent
- - Experience with workflow/pipeline orchestration (Airflow Step Functions or similar) and event-driven or service-oriented architectures.
- - Experience with container and cluster compute (Kubernetes/EKS Ray Slurm AWS Batch) and cloud infrastructure-as-code (AWS CDK/CloudFormation).
- - Experience building and operating fully-managed cloud services at scale including resiliency checkpointing and fault tolerance for long-running jobs.
- - Familiarity with machine-learning / deep-learning training workflows GPU/accelerator compute (SageMaker HyperPod AWS Trainium P5-class GPUs) or distributed-training frameworks (PyTorch FSDP Megatron-LM DeepSpeed verl).

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including health insurance (medical dental vision prescription Basic Life & AD&D insurance and option for Supplemental life plans EAP Mental Health Support Medical Advice Line Flexible Spending Accounts Adoption and Surrogacy Reimbursement coverage) 401(k) matching paid time off and parental leave. Learn more about our benefits at WA BELLEVUE - 143700.00 - 194400.00 USD annually