AI Platform Engineer

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

About eBay AI Platform

At eBay, we are building a next-generation AI platform to power intelligent, AI-driven experiences across our global marketplace. Our platform runs on large-scale Kubernetes-based compute infrastructure spanning on-premise GPU clusters, high-performance training environments, and hybrid cloud bursting to deliver GPU capacity at scale.

We focus on building resilient, high-performance Kubernetes-native infrastructure—spanning Custom Resource Definitions and operators, a custom AI-aware GPU scheduler, multi-NIC RDMA networking, GPU pool management, and distributed Ray compute via KubeRay—deployed across multiple availability zones with a global dispatcher for unified workload placement across training and inference pools.

About the Role

We are looking for an experienced Software Engineer specializing in Kubernetes and GPU Infrastructure to design and operate the foundational systems that power eBay's AI platform. You will own critical layers of our infrastructure—from Kubernetes CRD-based automation and a custom AI-aware GPU scheduler to RDMA-optimized multi-NIC GPU clusters and large-scale training environments—enabling our ML teams to train and serve AI models at eBay scale.

You will work on Kubernetes operator development, Gateway API and hybrid cloud networking, multi-NIC RDMA fabric design, a global GPU scheduler with cross-availability-zone dispatch, GPU pool management with provisioned throughput integration, topology-aware workload placement, and KubeRay infrastructure—partnering closely with ML Platform, AI Research, and Networking teams.

Key Responsibilities

• Design and build Kubernetes Custom Resource Definitions (CRDs) and operators for ML workloads, GPU node pools, and RayService CRDs.