h3Software Dev Mgr, ML Infrastructure, Edge AI Platform /h3 pJob ID: | Amazon.com Services LLC /p pAmazon 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. /p pToday, 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. /p pWe 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. /p pThis 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. /p h3Key job responsibilities /h3 ul liBuild, 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. /li liOwn the roadmap for ML infrastructure—distributed training, GPU capacity, workflow orchestration, CI/CD, and observability—balancing near‑term deliveries with long‑term platform health. /li liDrive 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. /li liEstablish 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. /li liPartner with applied science, compiler, runtime, hardware, security, and product teams to align requirements, manage dependencies,



and deliver cross‑team programs. /li /ul h3A day in the life /h3 ul liYou 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. /li liYou 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. /li /ul h3About the team /h3 pThe 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. /p pThe 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. /p h3Basic Qualifications /h3 ul li3+ years of engineering team management experience /li li7+ years of working directly within engineering teams experience /li li3+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience /li liKnowledge 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 /li liExperience partnering with product or program management teams /li liExperience managing a team of high calibre Software Engineers developing complex, world class,



scalable software systems that have been successfully delivered to customers /li /ul h3Preferred Qualifications /h3 ul liExperience in communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs and product strategy /li liExperience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers /li li7+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience /li liExperience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and PyTorch /li liExperience building or operating distributed systems or high‑performance computing systems /li liExperience leading teams that build distributed ML training, inference, evaluation, or data platforms using frameworks such as PyTorch, JAX, NeMo, or Megatron /li liExperience managing GPU cluster capacity, utilization, and cost at scale /li liExperience with model compression, quantization, knowledge distillation, model compilation, or edge deployment /li /ul pAmazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. /p pOur 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 you’re applying in isn't listed, please contact your Recruiting Partner. /p pThe 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 ADD 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 /p ul lihealth insurance (medical, dental, vision, prescription, Basic Life ADD insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage) /li li401(k) matching /li lipaid time off /li liparental leave /li /ul pUSA, WA, Bellevue - 184,900.00 - 250,200.00 USD annually /p #J-18808-Ljbffr

📌 Software Dev Mgr, ML Infrastructure, Edge AI Platform (Asti)
🏢 Amazon
📍 Asti