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 supports the full lifecycle of large-scale foundation models—from distributed pretraining on high-performance GPU clusters to high-throughput production inference—enabling commerce intelligence for hundreds of millions of users worldwide.

We focus on building state-of-the-art AI runtime infrastructure leveraging vLLM and TensorRT-LLM as pluggable inference engines behind a standardized AI runtime layer, alongside Megatron-LM and DeepSpeed for distributed training—integrated with provisioned throughput management, a distributed KV cache, prefill/decode disaggregation, and a robust MLOps stack spanning experiment management, fine-tuning automation, and production observability.

About the Role

We are looking for an experienced Software Engineer specializing in AI runtimes and MLOps to design and operate the systems that bring eBay's foundation models from research to production. You will own the inference runtime stack, the distributed training infrastructure, and the MLOps tooling that ties them together—enabling ML researchers and Applied Scientists to move fast without sacrificing reliability or performance.

You will work on production LLM/VLM inference serving with vLLM and TensorRT-LLM via a standardized AI runtime layer, implement distributed inference optimizations including prefill/decode disaggregation, distributed KV cache management, and LLM-aware request routing—develop large-scale distributed training pipelines using Megatron-LM and DeepSpeed on high-performance GPU clusters—and build the MLOps stack that automates the end-to-end model lifecycle.

Key Responsibilities