Software Dev Mgr, ML Infrastructure, Edge AI Platform

Amazon Devices (Lab126) builds products and services that delight millions of customers globally. The Edge AI ML Platform and Infrastructure team is building the platform that enables Amazon teams to train optimize evaluate and deploy generative AI models on devices and in the cloud.
Today optimizing a large model for a new hardware target requires experts to connect model onboarding distributed training compression evaluation compilation and deployment systems by hand. We are turning that work into a repeatable self-service workflow. Our platform supports large language vision audio multimodal and mixture-of-experts models and gives scientists and engineers the tools to move new optimization techniques from research code into reliable production workflows.
We are looking for a Software Development Manager to build and lead the ML infrastructure team behind this platform. You will own distributed training on multi-node GPU clusters compute capacity and utilization CI/CD observability and operational reliability for GPU-intensive workloads. You will hire and develop a team of software and ML infrastructure engineers set its technical direction and roadmap and deliver platform capabilities that scientists and product teams depend on to ship models with hundreds of billions of parameters.
This role combines people leadership with deep technical judgment. You will grow engineers and managers-in-the-making drive architecture decisions with your senior engineers turn ambiguous science and product needs into a prioritized plan and hold a high bar for delivery and operational excellence.


Key job responsibilities
Build lead and grow a team of software and ML infrastructure engineers: recruit and hire set clear goals coach for growth and manage performance across the team.
Own the roadmap for ML infrastructuredistributed training GPU capacity workflow orchestration CI/CD and observabilitybalancing near-term deliveries with long-term platform health.
Drive the architecture of distributed training capabilities (data tensor pipeline and model parallelism) for large language and multimodal models partnering with senior engineers and applied scientists.
Establish operational excellence for production platform services including metrics alarms runbooks on-call processes and root-cause correction of recurring issues while owning GPU fleet efficiency capacity planning and cost optimization.
Partner with applied science compiler runtime hardware security and product teams to align requirements manage dependencies and deliver cross-team programs.

A day in the life
You will move between people planning and technology. A typical day might include a 1:1 with an engineer on a growth plan a design review for a new training-orchestration capability triage of a failed multi-node training run a capacity review against upcoming model deliveries and a planning session with science leads on next quarters priorities.
You will use performance reliability cost and developer-productivity data to decide where the team invests. You will deliver incrementally while protecting long-term architecture and make sure the team fixes recurring problems at their root.

About the team
The Edge AI ML Platform and Infrastructure team brings together software engineers ML infrastructure engineers and GPU performance specialists. We build reusable model training optimization and deployment capabilities for Amazon product teams working closely with applied scientists across Edge AI. Our customers need to adapt rapidly changing model architectures to constrained hardware and production workloads without rebuilding the toolchain for every model.
The team owns the platform foundations that connect model development to deployment. Because our scope runs end to end we can improve training compression evaluation and deployment as one system. We value clear interfaces measurable performance automated quality gates and direct collaboration between science and engineering.

- 3 years of engineering team management experience
- 7 years of working directly within engineering teams experience
- 3 years of designing or architecting (design patterns reliability and scaling) of new and existing systems experience
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle including coding standards code reviews source control management build processes testing certification and livesite operations
- Experience partnering with product or program management teams
- Experience managing a team of high calibre Software Engineers developing complex world class scalable software systems that have been successfully delivered to customers

- Experience in communicating with users other technical teams and senior leadership to collect requirements describe software product features technical designs and product strategy
- Experience in recruiting hiring mentoring/coaching and managing teams of Software Engineers to improve their skills and make them more effective product software engineers
- 7 years of full software development life cycle including coding standards code reviews source control management build processes testing and operations experience
- Experience with Machine and Deep Learning toolkits such as MXNet TensorFlow Caffe and PyTorch
- Experience building or operating distributed systems or high-performance computing systems
- Experience leading teams that build distributed ML training inference evaluation or data platforms using frameworks such as PyTorch JAX NeMo or Megatron
- Experience managing GPU cluster capacity utilization and cost at scale
- Experience with model compression quantization knowledge distillation model compilation or edge deployment

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 - 184900.00 - 250200.00 USD annually