Senior Software Engineer II - Ads Quality at Instacart

Source: Instacart’s job board on Greenhouse, checked today.

Written by the listing’s owner or source, not by Openpath.

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview As a Senior Software Engineer II in the Ads Quality team specializing in ML infrastructure, you'll work with a team of software and machine learning engineers to build state-of-the-art systems that optimize Ads performance throughout the ads serving funnel, from retrieval to pricing. You'll own the technical direction of our data, feature processing and model training/serving solutions, enabling next-generation modelling opportunities—from utilizing out of the box LLMs to building bespoke Foundation Models that powers all the Ads use cases. You will eliminate bottlenecks across the training and serving system to optimize stability, latency and cost. Building on Instacart's unique data and market position, these systems create value for advertising partners, improve consumer experience, and directly contribute to Instacart's overall revenue / business success. You'll have the unique opportunity to collaborate with a cross-functional team, turning diverse insights into powerful solutions that elevate advertising effectiveness and operational stability. Your expertise will steer the development of scalable training and serving infrastructure, driving performance and efficiency gains across our advertising platform. Together, we'll build an ads ecosystem that not only serves millions but also excels in delivering value to users and advertisers alike. Instacart Ads is transforming the online grocery shopping experience by seamlessly integrating advanced advertising solutions into our platform. The Ads Quality team is dedicated to delivering outstanding advertising outcomes by driving innovation and precision in response prediction, advertiser optimization, and ads auction processes. Our mission is to ensure relevance, fairness, and performance for all participants in the ad ecosystem. By leveraging terabytes of data and sophisticated machine learning algorithms, we build advanced predictive models that enhance user experience, drive advertiser success, and sustain a thriving ad ecosystem. We're also at the forefront of integrating Large Language Models (LLMs), unlocking powerful new capabilities that are redefining how brands connect with consumers in the digital grocery space. Our next major bet is a real-time sequential foundation model for ads—and building the training and serving infrastructure to make it possible is central to this role. About the Job Design, develop, and deploy machine learning infrastructure to tackle practical challenges in our complex marketplace while advancing the training and serving platform. Work with the company wide ML Foundation team to develop solutions to meet Ads unique needs. Optimize training and serving funnel bottlenecks—profiling real systems and driving meaningful wins in throughput, cost, and latency, from GPU/accelerator utilization to tail latency at serving time. …