Category: Engineering

Introduction We’re excited to introduce an innovative Large Language Model (LLM) agent framework that reimagines how enterprises can harness the power of AI to streamline operations and boost productivity. At its core, this framework leverages Standard Operating Procedures (SOPs) to guide AI-driven execution, ensuring reliability and consistency in complex processes. Initial evaluations have shown remarkable […]

Introduction At Grab, we operate a set of services that manage and provide counts of various items. While this may seem straightforward, the scale at which this feature operates—benefiting millions of Grab users daily—introduces complexity. This feature is divided into three microservices: one for “writing” counts, another for handling “read” requests, and a third serving […]

Creating high quality listings with the right set of details can be time-consuming for sellers. To help, eBay has simplified the listing experience, integrating our magical listing technology with a guided, mobile-friendly listing flow to improve the ease, speed, and quality of listing creation for sellers. This simplified listing experience now integrates our magical listing […]

Introduction Although Grab is a tech company, not everyone is an engineer. Many team members don’t use GitLab daily, and Markdown’s quirks can be challenging for them. This made adopting the Docs-as-Code culture a hurdle, particularly for non-engineering teams responsible for key engineering-facing documents. In this article, we’ll discuss how we’ve streamlined the Docs-as-Code process […]

Introduction Hugo plays a pivotal role in enabling data ingestion for Grab’s data lake, managing over 4,000 pipelines onboarded by users. The stability of Hugo pipelines is contingent upon the health of both the data sources and various Hugo components. Given the complexity of this system, pipeline failures occasionally occur, necessitating user intervention when retry […]

At Grab, we’ve been working to perfect our Spark observability tools. Our initial solution, Iris, was developed to provide a custom, in-depth observability tool for Spark jobs. As described in our previous blog post, Iris collects and analyses metrics and metadata at the job level, providing insights into resource usage, performance, and query patterns across […]

Shoppers today expect quick delivery of their items, along with an ability to shop locally. eBay is addressing these needs by offering a suite of solutions that allow customers to find fast shipping/local items, while providing accurate delivery estimates and retail standards information on Search item cards. Discover Fast Shipping Items with EaseeBay is unique […]

The transformative world of Generative AI (GenAI), which refers to artificial intelligence systems capable of creating new content such as text, images, or music that is similar to human-generated content, has become integral to innovation, powering the next generation of AI-enabled applications. At Grab, it is crucial that every Grabber has access to these cutting-edge […]

eBay has named the winners of its latest University Machine Learning Challenge, an annual competition in which students compete to create innovative ML-powered solutions that solve for a real-world ecommerce problem. Each winning student will be awarded a 2025 summer internship at eBay: Wei Ren, University of WashingtonKangqi Wang, University of Southern CaliforniaHuapeng Zhou, University […]

The following was originally posted on LinkedIn by eBay Chief AI Officer Nitzan Mekel-Bobrov. At eBay, we’re leveraging the latest advances in AI to redefine the future of ecommerce for enthusiasts. The evolution from LLMs to agentic systems is happening faster than most of us would have predicted just a year ago, and we believe […]