Lead ML Infrastructure Manager – Edge AI Platform

Amazon Inc.; Factoria (WA); On-site; USD 185,000 - 250,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon.com Services LLC's Edge AI ML Platform and Infrastructure team seeks a Software Development Manager to lead the ML infrastructure behind model training, optimization, evaluation, and deployment on devices and in the cloud.

You will recruit and grow engineers, set technical direction, and own distributed training on multi‑node GPU clusters, CI/CD, and observability, balancing near‑term delivery with long‑term platform health.

Qualifications

• 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

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.

Skills

Engineering leadership

Distributed training

GPU capacity planning

CI/CD & observability

Cross-team collaboration

People development

Tools

MXNet

TensorFlow

PyTorch

JAX

Megatron

Kubernetes

Job description

Amazon.com Services LLC's Edge AI ML Platform and Infrastructure team seeks a Software Development Manager to lead the ML infrastructure behind model training, optimization, evaluation, and deployment on devices and in the cloud.

You will recruit and grow engineers, set technical direction, and own distributed training on multi‑node GPU clusters, CI/CD, and observability, balancing near‑term delivery with long‑term platform health.