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 infrastructure-distributed training, GPU capacity, workflow orchestration, CI/CD, and observability-balancing 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 quarter"s 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.